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1. Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks

Author: Robinson, R., Dou, Q., Castro, D. C., Kamnitsas, K., de Groot, M., Summers, R. M., Rueckert, D., and Glocker, B.
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: We investigate the use of image-and-spatial transformer networks (ISTNs) to tackle domain shift in multi-site medical imaging data. Commonly, domain adaptation (DA) is performed with little regard for explainability of the inter-domain transformation and is often conducted at the feature-level in the latent space. We employ ISTNs for DA at the image-level which constrains transformations to explainable appearance and shape changes. As proof-of-concept we demonstrate that ISTNs can be trained adversarially on a classification problem with simulated 2D data. For real-data validation, we construct two 3D brain MRI datasets from the Cam-CAN and UK Biobank studies to investigate domain shift due to acquisition and population differences. We show that age regression and sex classification models trained on ISTN output improve generalization when training on data from one and testing on the other site., Comment: Accepted at MICCAI 2020
Published: 2020
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2. A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy

Author: Mackay, K., Bernstein, D., Glocker, B., Kamnitsas, K., and Taylor, A.
Published: 2023
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3. Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Author: Kamnitsas, K., Bai, W., Ferrante, E., McDonagh, S., Sinclair, M., Pawlowski, N., Rajchl, M., Lee, M., Kainz, B., Rueckert, D., Glocker, B., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Crimi, Alessandro, editor, Bakas, Spyridon, editor, Kuijf, Hugo, editor, Menze, Bjoern, editor, and Reyes, Mauricio, editor
Published: 2018
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4. Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Author: Kamnitsas, K., primary, Bai, W., additional, Ferrante, E., additional, McDonagh, S., additional, Sinclair, M., additional, Pawlowski, N., additional, Rajchl, M., additional, Lee, M., additional, Kainz, B., additional, Rueckert, D., additional, and Glocker, B., additional
Published: 2018
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5. Relationship between Measures of Cerebrovascular Reactivity and Intracranial Lesion Progression in Acute Traumatic Brain Injury Patients: A CENTER-TBI Study

Author: Mathieu, F, Zeiler, F, Ercole, A, Monteiro, M, Kamnitsas, K, Glocker, B, Whitehouse, D, Das, T, Smielewski, P, Czosnyka, M, Hutchinson, P, Newcombe, V, Menon, D, Citerio, G, Mathieu F., Zeiler F. A., Ercole A., Monteiro M., Kamnitsas K., Glocker B., Whitehouse D. P., Das T., Smielewski P., Czosnyka M., Hutchinson P. J., Newcombe V. F. J., Menon D. K., Citerio G., Mathieu, F, Zeiler, F, Ercole, A, Monteiro, M, Kamnitsas, K, Glocker, B, Whitehouse, D, Das, T, Smielewski, P, Czosnyka, M, Hutchinson, P, Newcombe, V, Menon, D, Citerio, G, Mathieu F., Zeiler F. A., Ercole A., Monteiro M., Kamnitsas K., Glocker B., Whitehouse D. P., Das T., Smielewski P., Czosnyka M., Hutchinson P. J., Newcombe V. F. J., Menon D. K., and Citerio G.
Abstract: Failure of cerebral autoregulation has been linked to unfavorable outcome after traumatic brain injury (TBI). Preliminary evidence from a small, retrospective, single-center analysis suggests that autoregulatory dysfunction may be associated with traumatic lesion expansion, particularly for pericontusional edema. The goal of this study was to further explore these associations using prospective, multi-center data from the Collaborative European Neurotrauma Effectiveness Research in TBI (CENTER-TBI) and to further explore the relationship between autoregulatory failure, lesion progression, and patient outcome. A total of 88 subjects from the CENTER-TBI High Resolution ICU Sub-Study cohort were included. All patients had an admission computed tomography (CT) scan and early repeat scan available, as well as high-frequency neurophysiological recordings covering the between-scan interval. Using a novel, semiautomated approach at lesion segmentation, we calculated absolute changes in volume of contusion core, pericontusional edema, and extra-axial hemorrhage between the imaging studies. We then evaluated associations between cerebrovascular reactivity metrics and radiological lesion progression using mixed-model regression. Analyses were adjusted for baseline covariates and non-neurophysiological factors associated with lesion growth using multi-variate methods. Impairment in cerebrovascular reactivity was significantly associated with progression of pericontusional edema and, to a lesser degree, intraparenchymal hemorrhage. In contrast, there were no significant associations with extra-axial hemorrhage. The strongest relationships were observed between RAC-based metrics and edema formation. Pulse amplitude index showed weaker, but consistent, associations with contusion growth. Cerebrovascular reactivity metrics remained strongly associated with lesion progression after taking into account contributions from non-neurophysiological factors and mean cerebral perfusion pres
Published: 2020

6. Variation in Structure and Process of Care in Traumatic Brain Injury: Provider Profiles of European Neurotrauma Centers Participating in the CENTER-TBI Study

Author: Cnossen M. C., Polinder S., Lingsma H. F., Maas A. I. R., Menon D., Steyerberg E. W., Adams H., Alessandro M., Allanson J., Amrein K., Andaluz N., Andelic N., Andrea N., Andreassen L., Anke A., Antoni A., Ardon H., Audibert G., Auslands K., Azouvi P., Baciu C., Bacon A., Badenes R., Baglin T., Bartels R., Barzo P., Bauerfeind U., Beer R., Belda F. J., Bellander B. -M., Belli A., Bellier R., Benali H., Benard T., Berardino M., Beretta L., Beynon C., Bilotta F., Binder H., Biqiri E., Blaabjerg M., Borgen L. S., Bouzat P., Bragge P., Brazinova A., Brehar F., Brorsson C., Buki A., Bullinger M., Buckova V., Calappi E., Cameron P., Carbayo L. G., Carise E., Carpenter C., Castano-Leon A. M., Causin F., Chevallard G., Chieregato A., Citerio G., Coburn M. C., Coles J., Cooper J. D., Correia M., Covic A., Curry N., Czeiter E., Czosnyka M., Dahyot-Fizelier C., Damas F., Damas P., Dawes H., De Keyser V., Della Corte F., Depreitere B., Ding S., Dippel D., Dizdarevic K., Duliere G. -L., Dzeko A., Eapen G., Engemann H., Ercole A., Esser P., Ezer E., Fabricius M., Feigin V. L., Feng J., Foks K., Fossi F., Francony G., Frantzen J., Freo U., Frisvold S., Furmanov A., Gagliardo P., Galanaud D., Gao G., Geleijns K., Ghuysen A., Giraud B., Glocker B., Gomez P. A., Grossi F., Gruen R. L., Gupta D., Haagsma J. A., Hadzic E., Haitsma I., Hartings J. A., Helbok R., Helseth E., Hertle D., Hill S., Hoedemaekers A., Hoefer S., Hutchinson P. J., Haberg A. K., Jacobs B., Janciak I., Janssens K., Jiang J., Jones K., Kalala J. -P., Kamnitsas K., Karan M., Karau J., Katila A., Kaukonen M., Keeling D., Kerforne T., Ketharanathan N., Kettunen J., Kivisaari R., Kolias A. G., Kolumban B., Kompanje E., Kondziella D., Koskinen L. -O., Kovacs N., Kalovits F., Lagares A., Lanyon L., Laureys S., Lauritzen M., Lecky F., Ledig C., Lefering R., Legrand V., Lei J., Levi L., Lightfoot R., Lingsma H., Loeckx D., Lozano A., Luddington R., Luijten-Arts C., MacDonald S., MacFayden C., Maegele M., Majdan M., Major S., Manara A., Manhes P., Manley G., Martin D., Martino C., Maruenda A., Marechal H., Mastelova D., Mattern J., McMahon C., Melegh B., Menovsky T., Morganti-Kossmann C., Mulazzi D., Mutschler M., Muhlan H., Negru A., Nelson D., Neugebauer E., Newcombe V., Noirhomme Q., Nyiradi J., Oddo M., Oldenbeuving A., Oresic M., Ortolano F., Palotie A., Parizel P. M., Patruno A., Payen J. -F., Perera N., Perlbarg V., Persona P., Peul W., Pichon N., Piilgaard H., Piippo A., Pili F. S., Pirinen M., Ples H., Pomposo I., Psota M., Pullens P., Puybasset L., Ragauskas A., Raj R., Rambadagalla M., Rehorcikova V., Rhodes J., Richardson S., Ripatti S., Rocka S., Rodier N., Roe C., Roise O., Roks G., Romegoux P., Rosand J., Rosenfeld J., Rosenlund C., Rosenthal G., Rossaint R., Rossi S., Rostalski T., Rueckert D. L., Ruiz De Arcaute F., Rusnak M., Sacchi M., Sahakian B., Sahuquillo J., Sakowitz O., Sala F., Sanchez-Pena P., Sanchez-Porras R., Sandor J., Santos E., Sasse N., Sasu L., Savo D., Schipper I., Schlosser B., Schmidt S., Schneider A., Schoechl H., Schoonman G., Schou R. F., Schwendenwein E., Scholl M., Sir O., Skandsen T., Smakman L., Smeets D., Smielewski P., Sorinola A., Stamatakis E. L., Stanworth S., Stegemann K., Steinbuchel N., Stevens R., Stewart W., Stocchetti N., Sundstrom N., Synnot A., Szabo J., Soderberg J., Taccone F. S., Tamas V., Tanskanen P., Tascu A., Taylor M. S., Te Ao B., Tenovuo O., Teodorani G., Theadom A., Thomas M., Tibboel D., Tolias C., Tshibanda J. -F. L., Tudora C. M., Vajkoczy P., Valeinis E., Van Hecke W., Van Praag D., Van Roost D., Van Vlierberghe E., Vande Vyvere T., Vanhaudenhuyse A., Vargiolu A., Vega E., Verheyden J., Vespa P. M., Vik A., Vilcinis R., Vizzino G., Vleggeert-Lankamp C., Volovici V., Vulekovic P., Vamos Z., Wade D., Wang K. K. W., Wang L., Wildschut E., Williams G., Willumsen L., Wilson A., Wilson L., Winkler M. K. L., Ylen P., Younsi A., Zaaroor M., Zhang Z., Zheng Z., Zumbo F., De Lange S., De Ruiter G. C. W., Den Boogert H., Van Dijck J., Van Essen T. A., Van Heugten C., Van Der Jagt M., Van Der Naalt J., CENTER-Tbi Invest Participants, Cnossen, M, Polinder, S, Lingsma, H, Maas, A, Menon, D, Steyerberg, E, Adams, H, Alessandro, M, Allanson, J, Amrein, K, Andaluz, N, Andelic, N, Andrea, N, Andreassen, L, Anke, A, Antoni, A, Ardon, H, Audibert, G, Auslands, K, Azouvi, P, Baciu, C, Bacon, A, Badenes, R, Baglin, T, Bartels, R, Barzo, P, Bauerfeind, U, Beer, R, Belda, F, Bellander, B, Belli, A, Bellier, R, Benali, H, Benard, T, Berardino, M, Beretta, L, Beynon, C, Bilotta, F, Binder, H, Biqiri, E, Blaabjerg, M, Borgen, L, Bouzat, P, Bragge, P, Brazinova, A, Brehar, F, Brorsson, C, Buki, A, Bullinger, M, Buckova, V, Calappi, E, Cameron, P, Carbayo, L, Carise, E, Carpenter, C, Castano-Leon, A, Causin, F, Chevallard, G, Chieregato, A, Citerio, G, Coburn, M, Coles, J, Cooper, J, Correia, M, Covic, A, Curry, N, Czeiter, E, Czosnyka, M, Dahyot-Fizelier, C, Damas, F, Damas, P, Dawes, H, De Keyser, V, Della Corte, F, Depreitere, B, Ding, S, Dippel, D, Dizdarevic, K, Duliere, G, Dzeko, A, Eapen, G, Engemann, H, Ercole, A, Esser, P, Ezer, E, Fabricius, M, Feigin, V, Feng, J, Foks, K, Fossi, F, Francony, G, Frantzen, J, Freo, U, Frisvold, S, Furmanov, A, Gagliardo, P, Galanaud, D, Gao, G, Geleijns, K, Ghuysen, A, Giraud, B, Glocker, B, Gomez, P, Grossi, F, Gruen, R, Gupta, D, Haagsma, J, Hadzic, E, Haitsma, I, Hartings, J, Helbok, R, Helseth, E, Hertle, D, Hill, S, Hoedemaekers, A, Hoefer, S, Hutchinson, P, Haberg, A, Jacobs, B, Janciak, I, Janssens, K, Jiang, J, Jones, K, Kalala, J, Kamnitsas, K, Karan, M, Karau, J, Katila, A, Kaukonen, M, Keeling, D, Kerforne, T, Ketharanathan, N, Kettunen, J, Kivisaari, R, Kolias, A, Kolumban, B, Kompanje, E, Kondziella, D, Koskinen, L, Kovacs, N, Kalovits, F, Lagares, A, Lanyon, L, Laureys, S, Lauritzen, M, Lecky, F, Ledig, C, Lefering, R, Legrand, V, Lei, J, Levi, L, Lightfoot, R, Loeckx, D, Lozano, A, Luddington, R, Luijten-Arts, C, Macdonald, S, Macfayden, C, Maegele, M, Majdan, M, Major, S, Manara, A, Manhes, P, Manley, G, Martin, D, Martino, C, Maruenda, A, Marechal, H, Mastelova, D, Mattern, J, Mcmahon, C, Melegh, B, Menovsky, T, Morganti-Kossmann, C, Mulazzi, D, Mutschler, M, Muhlan, H, Negru, A, Nelson, D, Neugebauer, E, Newcombe, V, Noirhomme, Q, Nyiradi, J, Oddo, M, Oldenbeuving, A, Oresic, M, Ortolano, F, Palotie, A, Parizel, P, Patruno, A, Payen, J, Perera, N, Perlbarg, V, Persona, P, Peul, W, Pichon, N, Piilgaard, H, Piippo, A, Pili, F, Pirinen, M, Ples, H, Pomposo, I, Psota, M, Pullens, P, Puybasset, L, Ragauskas, A, Raj, R, Rambadagalla, M, Rehorcikova, V, Rhodes, J, Richardson, S, Ripatti, S, Rocka, S, Rodier, N, Roe, C, Roise, O, Roks, G, Romegoux, P, Rosand, J, Rosenfeld, J, Rosenlund, C, Rosenthal, G, Rossaint, R, Rossi, S, Rostalski, T, Rueckert, D, Ruiz De Arcaute, F, Rusnak, M, Sacchi, M, Sahakian, B, Sahuquillo, J, Sakowitz, O, Sala, F, Sanchez-Pena, P, Sanchez-Porras, R, Sandor, J, Santos, E, Sasse, N, Sasu, L, Savo, D, Schipper, I, Schlosser, B, Schmidt, S, Schneider, A, Schoechl, H, Schoonman, G, Schou, R, Schwendenwein, E, Scholl, M, Sir, O, Skandsen, T, Smakman, L, Smeets, D, Smielewski, P, Sorinola, A, Stamatakis, E, Stanworth, S, Stegemann, K, Steinbuchel, N, Stevens, R, Stewart, W, Stocchetti, N, Sundstrom, N, Synnot, A, Szabo, J, Soderberg, J, Taccone, F, Tamas, V, Tanskanen, P, Tascu, A, Taylor, M, Te Ao, B, Tenovuo, O, Teodorani, G, Theadom, A, Thomas, M, Tibboel, D, Tolias, C, Tshibanda, J, Tudora, C, Vajkoczy, P, Valeinis, E, Van Hecke, W, Van Praag, D, Van Roost, D, Van Vlierberghe, E, Vande Vyvere, T, Vanhaudenhuyse, A, Vargiolu, A, Vega, E, Verheyden, J, Vespa, P, Vik, A, Vilcinis, R, Vizzino, G, Vleggeert-Lankamp, C, Volovici, V, Vulekovic, P, Vamos, Z, Wade, D, Wang, K, Wang, L, Wildschut, E, Williams, G, Willumsen, L, Wilson, A, Wilson, L, Winkler, M, Ylen, P, Younsi, A, Zaaroor, M, Zhang, Z, Zheng, Z, Zumbo, F, De Lange, S, De Ruiter, G, Den Boogert, H, Van Dijck, J, Van Essen, T, Van Heugten, C, Van Der Jagt, M, Van Der Naalt, J, Cnossen, M. C., Polinder, S., Lingsma, H. F., Maas, A. I. R., Menon, D., Steyerberg, E. W., Adams, H., Alessandro, M., Allanson, J., Amrein, K., Andaluz, N., Andelic, N., Andrea, N., Andreassen, L., Anke, A., Antoni, A., Ardon, H., Audibert, G., Auslands, K., Azouvi, P., Baciu, C., Bacon, A., Badenes, R., Baglin, T., Bartels, R., Barzo, P., Bauerfeind, U., Beer, R., Belda, F. J., Bellander, B. -M., Belli, A., Bellier, R., Benali, H., Benard, T., Berardino, M., Beretta, L., Beynon, C., Bilotta, F., Binder, H., Biqiri, E., Blaabjerg, M., Borgen, L. S., Bouzat, P., Bragge, P., Brazinova, A., Brehar, F., Brorsson, C., Buki, A., Bullinger, M., Buckova, V., Calappi, E., Cameron, P., Carbayo, L. G., Carise, E., Carpenter, C., Castano-Leon, A. M., Causin, F., Chevallard, G., Chieregato, A., Citerio, G., Coburn, M. C., Coles, J., Cooper, J. D., Correia, M., Covic, A., Curry, N., Czeiter, E., Czosnyka, M., Dahyot-Fizelier, C., Damas, F., Damas, P., Dawes, H., De Keyser, V., Della Corte, F., Depreitere, B., Ding, S., Dippel, D., Dizdarevic, K., Duliere, G. -L., Dzeko, A., Eapen, G., Engemann, H., Ercole, A., Esser, P., Ezer, E., Fabricius, M., Feigin, V. L., Feng, J., Foks, K., Fossi, F., Francony, G., Frantzen, J., Freo, U., Frisvold, S., Furmanov, A., Gagliardo, P., Galanaud, D., Gao, G., Geleijns, K., Ghuysen, A., Giraud, B., Glocker, B., Gomez, P. A., Grossi, F., Gruen, R. L., Gupta, D., Haagsma, J. A., Hadzic, E., Haitsma, I., Hartings, J. A., Helbok, R., Helseth, E., Hertle, D., Hill, S., Hoedemaekers, A., Hoefer, S., Hutchinson, P. J., Haberg, A. K., Jacobs, B., Janciak, I., Janssens, K., Jiang, J., Jones, K., Kalala, J. -P., Kamnitsas, K., Karan, M., Karau, J., Katila, A., Kaukonen, M., Keeling, D., Kerforne, T., Ketharanathan, N., Kettunen, J., Kivisaari, R., Kolias, A. G., Kolumban, B., Kompanje, E., Kondziella, D., Koskinen, L. -O., Kovacs, N., Kalovits, F., Lagares, A., Lanyon, L., Laureys, S., Lauritzen, M., Lecky, F., Ledig, C., Lefering, R., Legrand, V., Lei, J., Levi, L., Lightfoot, R., Lingsma, H., Loeckx, D., Lozano, A., Luddington, R., Luijten-Arts, C., Macdonald, S., Macfayden, C., Maegele, M., Majdan, M., Major, S., Manara, A., Manhes, P., Manley, G., Martin, D., Martino, C., Maruenda, A., Marechal, H., Mastelova, D., Mattern, J., Mcmahon, C., Melegh, B., Menovsky, T., Morganti-Kossmann, C., Mulazzi, D., Mutschler, M., Muhlan, H., Negru, A., Nelson, D., Neugebauer, E., Newcombe, V., Noirhomme, Q., Nyiradi, J., Oddo, M., Oldenbeuving, A., Oresic, M., Ortolano, F., Palotie, A., Parizel, P. M., Patruno, A., Payen, J. -F., Perera, N., Perlbarg, V., Persona, P., Peul, W., Pichon, N., Piilgaard, H., Piippo, A., Pili, F. S., Pirinen, M., Ples, H., Pomposo, I., Psota, M., Pullens, P., Puybasset, L., Ragauskas, A., Raj, R., Rambadagalla, M., Rehorcikova, V., Rhodes, J., Richardson, S., Ripatti, S., Rocka, S., Rodier, N., Roe, C., Roise, O., Roks, G., Romegoux, P., Rosand, J., Rosenfeld, J., Rosenlund, C., Rosenthal, G., Rossaint, R., Rossi, S., Rostalski, T., Rueckert, D. L., Ruiz De Arcaute, F., Rusnak, M., Sacchi, M., Sahakian, B., Sahuquillo, J., Sakowitz, O., Sala, F., Sanchez-Pena, P., Sanchez-Porras, R., Sandor, J., Santos, E., Sasse, N., Sasu, L., Savo, D., Schipper, I., Schlosser, B., Schmidt, S., Schneider, A., Schoechl, H., Schoonman, G., Schou, R. F., Schwendenwein, E., Scholl, M., Sir, O., Skandsen, T., Smakman, L., Smeets, D., Smielewski, P., Sorinola, A., Stamatakis, E. L., Stanworth, S., Stegemann, K., Steinbuchel, N., Stevens, R., Stewart, W., Stocchetti, N., Sundstrom, N., Synnot, A., Szabo, J., Soderberg, J., Taccone, F. S., Tamas, V., Tanskanen, P., Tascu, A., Taylor, M. S., Te Ao, B., Tenovuo, O., Teodorani, G., Theadom, A., Thomas, M., Tibboel, D., Tolias, C., Tshibanda, J. -F. L., Tudora, C. M., Vajkoczy, P., Valeinis, E., Van Hecke, W., Van Praag, D., Van Roost, D., Van Vlierberghe, E., Vande Vyvere, T., Vanhaudenhuyse, A., Vargiolu, A., Vega, E., Verheyden, J., Vespa, P. M., Vik, A., Vilcinis, R., Vizzino, G., Vleggeert-Lankamp, C., Volovici, V., Vulekovic, P., Vamos, Z., Wade, D., Wang, K. K. W., Wang, L., Wildschut, E., Williams, G., Willumsen, L., Wilson, A., Wilson, L., Winkler, M. K. L., Ylen, P., Younsi, A., Zaaroor, M., Zhang, Z., Zheng, Z., Zumbo, F., De Lange, S., De Ruiter, G. C. W., Den Boogert, H., Van Dijck, J., Van Essen, T. A., Van Heugten, C., Van Der Jagt, M., Van Der Naalt, J., Commission of the European Communities, Molecular Neuroscience and Ageing Research (MOLAR), Menon, David [0000-0002-3228-9692], Apollo - University of Cambridge Repository, and Public Health
Subjects: Questionnaires, Comparative Effectiveness Research, Critical Care and Emergency Medicine, Medical Doctors, Neurologi, Health Care Providers, CENTER-TBI Investigators and Participants, lcsh:Medicine, Poison control, Occupational safety and health, Diagnostic Radiology, Geographical Locations, 0302 clinical medicine, Trauma Centers, Surveys and Questionnaires, Brain Injuries, Traumatic, Medicine and Health Sciences, Brain Damage, Longitudinal Studies, Prospective Studies, Israel, lcsh:Science, Tomography, Trauma Medicine, Multidisciplinary, Radiology and Imaging, traumatic brain injury, Trauma center, Hospitals, 3. Good health, Europe, Hospitalization, Professions, Intensive Care Units, Neurology, Research Design, Intracranial pressure monitoring, Medical emergency, Engineering sciences. Technology, Research Article, medicine.medical_specialty, Imaging Techniques, Traumatic brain injury, General Science & Technology, Concordance, Comparative effectiveness research, Neuroimaging, Research and Analysis Methods, 03 medical and health sciences, Diagnostic Medicine, Physicians, Injury prevention, MD Multidisciplinary, medicine, Humans, Treatment Guidelines, Survey Research, Health Care Policy, business.industry, lcsh:R, Biology and Life Sciences, 030208 emergency & critical care medicine, Length of Stay, medicine.disease, ta3124, Computed Axial Tomography, Health Care, Health Care Facilities, Family medicine, People and Places, lcsh:Q, Population Groupings, business, 030217 neurology & neurosurgery, Neuroscience
Abstract: © 2016 Cnossen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Introduction: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Methods: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. Results: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. Conclusion: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches.
Published: 2020

7. Correction to: Variation in neurosurgical management of traumatic brain injury: a survey in 68 centers participating in the CENTER-TBI study (Acta Neurochirurgica, (2019), 161, 3, (435-449), 10.1007/s00701-018-3761-z)

Author: van Essen T. A., van Essen, T, den Boogert, H, Cnossen, M, de Ruiter, G, Haitsma, I, Polinder, S, Steyerberg, E, Menon, D, Maas, A, Lingsma, H, Peul, W, Ackerlund, C, Adams, H, Agnoletti, V, Allanson, J, Amrein, K, Andaluz, N, Andelic, N, Andreassen, L, Antun, A, Anke, A, Antoni, A, Ardon, H, Audibert, G, Auslands, K, Azouvi, P, Azzolini, M, Baciu, C, Badenes, R, Bartels, R, Larizza, D, Barzo, P, Bauerfeind, U, Beauvais, R, Beer, R, Belda, F, Bellander, B, Belli, A, Bellier, R, Benali, H, Benard, T, Berardino, M, Beretta, L, Beynon, C, Bilotta, F, Binder, H, Biqiri, E, Blaabjerg, M, Bouzat, P, Bragge, P, Brazinova, A, Brinck, V, Brooker, J, Brorsson, C, Buki, A, Bullinger, M, Calappi, E, Calvi, M, Cameron, P, Carbayo, L, Carbonara, M, Carise, E, Carpenter, K, Castano-Leon, A, Causin, F, Chevallard, G, Chieregato, A, Citerio, G, Coburn, M, Coles, J, Coles-Kemp, L, Collett, J, Cooper, J, Correia, M, Covic, A, Curry, N, Czeiter, E, Czosnyka, M, Dahyot-Fizelier, C, Damas, F, Damas, P, Dawes, H, De Keyser, V, Della, C, Depreitere, B, Dilvesi, D, Ding, S, Dippel, D, Dixit, A, Donoghue, E, Dreier, J, Duliere, G, Eapen, G, Engemann, H, Ercole, A, Esser, P, Ezer, E, Fabricius, M, Feigin, V, Feng, J, Foks, K, Fossi, F, Francony, G, Freo, U, Frisvold, S, Furmanov, A, Gagliardo, P, Galanaud, D, Gantner, D, Gao, G, Geleijns, K, George, P, Ghuysen, A, Giga, L, Giraud, B, Glocker, B, Golubovic, J, Gomez, P, Grossi, F, Gruen, R, Gupta, D, Haagsma, J, Hartings, J, Helbok, R, Helseth, E, Hertle, D, Hoedemaekers, A, Hoefer, S, Horton, L, Huijben, J, Hutchinson, P, Haberg, A, Jacobs, B, Jankowski, S, Jarrett, M, Jelaca, B, Jiang, J, Jones, K, Kamnitsas, K, Karan, M, Katila, A, Kaukonen, M, Kerforne, T, Kivisaari, R, Kolias, A, Kolumban, B, Kompanje, E, Kolundzija, K, Kondziella, D, Koskinen, L, Kovacs, N, Lagares, A, Lanyon, L, Laureys, S, Lecky, F, Ledig, C, Lefering, R, Legrand, V, Lei, J, Levi, L, Lightfoot, R, Loeckx, D, Lozano, A, Macdonald, S, Maegele, M, Majdan, M, Major, S, Manara, A, Manley, G, Martin, D, Martin, L, Martino, C, Maruenda, A, Marechal, H, Masala, A, Mattern, J, Mcfadyen, C, Mcmahon, C, Melegh, B, Menovsky, T, Morganti-Kossmann, C, Mulazzi, D, Muraleedharan, V, Murray, L, Muhlan, H, Nair, N, Negru, A, Nelson, D, Newcombe, V, Nieboer, D, Noirhomme, Q, Nyiradi, J, Oddo, M, Oldenbeuving, A, Oresic, M, Ortolano, F, Palotie, A, Parizel, P, Patruno, A, Payen, J, Perera, N, Perlbarg, V, Persona, P, Piippo-Karjalainen, A, Pili, F, Pirinen, M, Ples, H, Poca, M, Pomposo, I, Posti, J, Puybasset, L, Radoi, A, Ragauskas, A, Raj, R, Rambadagalla, M, Real, R, Rehorcikova, V, Rhodes, J, Ripatti, S, Rocka, S, Roe, C, Roise, O, Roks, G, Rosand, J, Rosenfeld, J, Rosenlund, C, Rosenthal, G, Rossaint, R, Rossi, S, Rueckert, D, Rusnak, M, Sacchi, M, Sahakian, B, Sahuquillo, J, Sakowitz, O, Sala, F, Sanchez-Porras, R, Sandor, J, Santos, E, Sasu, L, Savo, D, Schaffer, N, Schipper, I, Schlosser, B, Schmidt, S, Schoechl, H, Schoonman, G, Schou, R, Schwendenwein, E, Scholl, M, Sir, O, Skandsen, T, Smakman, L, Smeets, D, Smielewski, P, Sorinola, A, Stamatakis, E, Stanworth, S, Steinbuchel, N, Stevanovic, A, Stevens, R, Stewart, W, Stocchetti, N, Sundstrom, N, Synnot, A, Taccone, F, Takala, R, Tamas, V, Tanskanen, P, Taylor, M, Te Ao, B, Tenovuo, O, Telgmann, R, Teodorani, G, Theadom, A, Thomas, M, Tibboel, D, Tolias, C, Tshibanda, J, Trapani, T, Tudora, C, Vajkoczy, P, Vallance, S, Valeinis, E, Van der Steen, G, van der Jagt, M, van der Naalt, J, van Dijck, J, Van Hecke, W, van Heugten, C, Van Praag, D, Vande Vyvere, T, Van Waesberghe, J, Vanhaudenhuyse, A, Vargiolu, A, Vega, E, Velt, K, Verheyden, J, Vespa, P, Vik, A, Vilcinis, R, Vizzino, G, Vleggeert-Lankamp, C, Volovici, V, Voormolen, D, Vulekovic, P, Vamos, Z, Wade, D, Wang, K, Wang, L, Wessels, L, Wildschut, E, Williams, G, Wilson, L, Winkler, M, Wolf, S, Ylen, P, Younsi, A, Zaaroor, M, Zhihui, Y, Ziverte, A, Zumbo, F, van Essen T. A., den Boogert H. F., Cnossen M. C., de Ruiter G. C. W., Haitsma I., Polinder S., Steyerberg E. W., Menon D., Maas A. I. R., Lingsma H. F., Peul W. C., Ackerlund C., Adams H., Agnoletti V., Allanson J., Amrein K., Andaluz N., Andelic N., Andreassen L., Antun A., Anke A., Antoni A., Ardon H., Audibert G., Auslands K., Azouvi P., Azzolini M. L., Baciu C., Badenes R., Bartels R., Larizza D., Barzo P., Bauerfeind U., Beauvais R., Beer R., Belda F. J., Bellander B. -M., Belli A., Bellier R., Benali H., Benard T., Berardino M., Beretta L., Beynon C., Bilotta F., Binder H., Biqiri E., Blaabjerg M., den Boogert H., Bouzat P., Bragge P., Brazinova A., Brinck V., Brooker J., Brorsson C., Buki A., Bullinger M., Calappi E., Calvi M. R., Cameron P., Carbayo L. G., Carbonara M., Carise E., Carpenter K., Castano-Leon A. M., Causin F., Chevallard G., Chieregato A., Citerio G., Cnossen M., Coburn M., Coles J., Coles-Kemp L., Collett J., Cooper J. D., Correia M., Covic A., Curry N., Czeiter E., Czosnyka M., Dahyot-Fizelier C., Damas F., Damas P., Dawes H., De Keyser V., Della C. F., Depreitere B., Dilvesi D., Ding S., Dippel D., Dixit A., Donoghue E., Dreier J., Duliere G. -L., Eapen G., Engemann H., Ercole A., Esser P., Ezer E., Fabricius M., Feigin V. L., Feng J., Foks K., Fossi F., Francony G., Freo U., Frisvold S., Furmanov A., Gagliardo P., Galanaud D., Gantner D., Gao G., Geleijns K., George P., Ghuysen A., Giga L., Giraud B., Glocker B., Golubovic J., Gomez P. A., Grossi F., Gruen R. L., Gupta D., Haagsma J. A., Hartings J. A., Helbok R., Helseth E., Hertle D., Hoedemaekers A., Hoefer S., Horton L., Huijben J., Hutchinson P. J., Haberg A. K., Jacobs B., Jankowski S., Jarrett M., Jelaca B., Jiang J. -Y., Jones K., Kamnitsas K., Karan M., Katila A., Kaukonen M., Kerforne T., Kivisaari R., Kolias A. G., Kolumban B., Kompanje E., Kolundzija K., Kondziella D., Koskinen L. -O., Kovacs N., Lagares A., Lanyon L., Laureys S., Lecky F., Ledig C., Lefering R., Legrand V., Lei J., Levi L., Lightfoot R., Lingsma H., Loeckx D., Lozano A., MacDonald S., Maegele M., Majdan M., Major S., Manara A., Manley G., Martin D., Martin L. F., Martino C., Maruenda A., Marechal H., Masala A., Mattern J., McFadyen C., McMahon C., Melegh B., Menovsky T., Morganti-Kossmann C., Mulazzi D., Muraleedharan V., Murray L., Muhlan H., Nair N., Negru A., Nelson D., Newcombe V., Nieboer D., Noirhomme Q., Nyiradi J., Oddo M., Oldenbeuving A., Oresic M., Ortolano F., Palotie A., Parizel P. M., Patruno A., Payen J. -F., Perera N., Perlbarg V., Persona P., Peul W., Piippo-Karjalainen A., Pili F. S., Pirinen M., Ples H., Poca M. A., Pomposo I., Posti J., Puybasset L., Radoi A., Ragauskas A., Raj R., Rambadagalla M., Real R., Rehorcikova V., Rhodes J., Ripatti S., Rocka S., Roe C., Roise O., Roks G., Rosand J., Rosenfeld J., Rosenlund C., Rosenthal G., Rossaint R., Rossi S., Rueckert D., Rusnak M., Sacchi M., Sahakian B., Sahuquillo J., Sakowitz O., Sala F., Sanchez-Porras R., Sandor J., Santos E., Sasu L., Savo D., Schaffer N., Schipper I., Schlosser B., Schmidt S., Schoechl H., Schoonman G., Schou R. F., Schwendenwein E., Scholl M., Sir O., Skandsen T., Smakman L., Smeets D., Smielewski P., Sorinola A., Stamatakis E., Stanworth S., Steinbuchel N., Stevanovic A., Stevens R., Stewart W., Stocchetti N., Sundstrom N., Synnot A., Taccone F. S., Takala R., Tamas V., Tanskanen P., Taylor M. S., Te Ao B., Tenovuo O., Telgmann R., Teodorani G., Theadom A., Thomas M., Tibboel D., Tolias C., Tshibanda J. -F. L., Trapani T., Tudora C. M., Vajkoczy P., Vallance S., Valeinis E., Van der Steen G., van der Jagt M., van der Naalt J., van Dijck J. T. J. M., Van Hecke W., van Heugten C., Van Praag D., Vande Vyvere T., Van Waesberghe J., Vanhaudenhuyse A., Vargiolu A., Vega E., Velt K., Verheyden J., Vespa P. M., Vik A., Vilcinis R., Vizzino G., Vleggeert-Lankamp C., Volovici V., Voormolen D., Vulekovic P., Vamos Z., Wade D., Wang K. K. W., Wang L., Wessels L., Wildschut E., Williams G., Wilson L., Winkler M. K. L., Wolf S., Ylen P., Younsi A., Zaaroor M., Zhihui Y., Ziverte A., Zumbo F., van Essen T. A., van Essen, T, den Boogert, H, Cnossen, M, de Ruiter, G, Haitsma, I, Polinder, S, Steyerberg, E, Menon, D, Maas, A, Lingsma, H, Peul, W, Ackerlund, C, Adams, H, Agnoletti, V, Allanson, J, Amrein, K, Andaluz, N, Andelic, N, Andreassen, L, Antun, A, Anke, A, Antoni, A, Ardon, H, Audibert, G, Auslands, K, Azouvi, P, Azzolini, M, Baciu, C, Badenes, R, Bartels, R, Larizza, D, Barzo, P, Bauerfeind, U, Beauvais, R, Beer, R, Belda, F, Bellander, B, Belli, A, Bellier, R, Benali, H, Benard, T, Berardino, M, Beretta, L, Beynon, C, Bilotta, F, Binder, H, Biqiri, E, Blaabjerg, M, Bouzat, P, Bragge, P, Brazinova, A, Brinck, V, Brooker, J, Brorsson, C, Buki, A, Bullinger, M, Calappi, E, Calvi, M, Cameron, P, Carbayo, L, Carbonara, M, Carise, E, Carpenter, K, Castano-Leon, A, Causin, F, Chevallard, G, Chieregato, A, Citerio, G, Coburn, M, Coles, J, Coles-Kemp, L, Collett, J, Cooper, J, Correia, M, Covic, A, Curry, N, Czeiter, E, Czosnyka, M, Dahyot-Fizelier, C, Damas, F, Damas, P, Dawes, H, De Keyser, V, Della, C, Depreitere, B, Dilvesi, D, Ding, S, Dippel, D, Dixit, A, Donoghue, E, Dreier, J, Duliere, G, Eapen, G, Engemann, H, Ercole, A, Esser, P, Ezer, E, Fabricius, M, Feigin, V, Feng, J, Foks, K, Fossi, F, Francony, G, Freo, U, Frisvold, S, Furmanov, A, Gagliardo, P, Galanaud, D, Gantner, D, Gao, G, Geleijns, K, George, P, Ghuysen, A, Giga, L, Giraud, B, Glocker, B, Golubovic, J, Gomez, P, Grossi, F, Gruen, R, Gupta, D, Haagsma, J, Hartings, J, Helbok, R, Helseth, E, Hertle, D, Hoedemaekers, A, Hoefer, S, Horton, L, Huijben, J, Hutchinson, P, Haberg, A, Jacobs, B, Jankowski, S, Jarrett, M, Jelaca, B, Jiang, J, Jones, K, Kamnitsas, K, Karan, M, Katila, A, Kaukonen, M, Kerforne, T, Kivisaari, R, Kolias, A, Kolumban, B, Kompanje, E, Kolundzija, K, Kondziella, D, Koskinen, L, Kovacs, N, Lagares, A, Lanyon, L, Laureys, S, Lecky, F, Ledig, C, Lefering, R, Legrand, V, Lei, J, Levi, L, Lightfoot, R, Loeckx, D, Lozano, A, Macdonald, S, Maegele, M, Majdan, M, Major, S, Manara, A, Manley, G, Martin, D, Martin, L, Martino, C, Maruenda, A, Marechal, H, Masala, A, Mattern, J, Mcfadyen, C, Mcmahon, C, Melegh, B, Menovsky, T, Morganti-Kossmann, C, Mulazzi, D, Muraleedharan, V, Murray, L, Muhlan, H, Nair, N, Negru, A, Nelson, D, Newcombe, V, Nieboer, D, Noirhomme, Q, Nyiradi, J, Oddo, M, Oldenbeuving, A, Oresic, M, Ortolano, F, Palotie, A, Parizel, P, Patruno, A, Payen, J, Perera, N, Perlbarg, V, Persona, P, Piippo-Karjalainen, A, Pili, F, Pirinen, M, Ples, H, Poca, M, Pomposo, I, Posti, J, Puybasset, L, Radoi, A, Ragauskas, A, Raj, R, Rambadagalla, M, Real, R, Rehorcikova, V, Rhodes, J, Ripatti, S, Rocka, S, Roe, C, Roise, O, Roks, G, Rosand, J, Rosenfeld, J, Rosenlund, C, Rosenthal, G, Rossaint, R, Rossi, S, Rueckert, D, Rusnak, M, Sacchi, M, Sahakian, B, Sahuquillo, J, Sakowitz, O, Sala, F, Sanchez-Porras, R, Sandor, J, Santos, E, Sasu, L, Savo, D, Schaffer, N, Schipper, I, Schlosser, B, Schmidt, S, Schoechl, H, Schoonman, G, Schou, R, Schwendenwein, E, Scholl, M, Sir, O, Skandsen, T, Smakman, L, Smeets, D, Smielewski, P, Sorinola, A, Stamatakis, E, Stanworth, S, Steinbuchel, N, Stevanovic, A, Stevens, R, Stewart, W, Stocchetti, N, Sundstrom, N, Synnot, A, Taccone, F, Takala, R, Tamas, V, Tanskanen, P, Taylor, M, Te Ao, B, Tenovuo, O, Telgmann, R, Teodorani, G, Theadom, A, Thomas, M, Tibboel, D, Tolias, C, Tshibanda, J, Trapani, T, Tudora, C, Vajkoczy, P, Vallance, S, Valeinis, E, Van der Steen, G, van der Jagt, M, van der Naalt, J, van Dijck, J, Van Hecke, W, van Heugten, C, Van Praag, D, Vande Vyvere, T, Van Waesberghe, J, Vanhaudenhuyse, A, Vargiolu, A, Vega, E, Velt, K, Verheyden, J, Vespa, P, Vik, A, Vilcinis, R, Vizzino, G, Vleggeert-Lankamp, C, Volovici, V, Voormolen, D, Vulekovic, P, Vamos, Z, Wade, D, Wang, K, Wang, L, Wessels, L, Wildschut, E, Williams, G, Wilson, L, Winkler, M, Wolf, S, Ylen, P, Younsi, A, Zaaroor, M, Zhihui, Y, Ziverte, A, Zumbo, F, van Essen T. A., den Boogert H. F., Cnossen M. C., de Ruiter G. C. W., Haitsma I., Polinder S., Steyerberg E. W., Menon D., Maas A. I. R., Lingsma H. F., Peul W. C., Ackerlund C., Adams H., Agnoletti V., Allanson J., Amrein K., Andaluz N., Andelic N., Andreassen L., Antun A., Anke A., Antoni A., Ardon H., Audibert G., Auslands K., Azouvi P., Azzolini M. L., Baciu C., Badenes R., Bartels R., Larizza D., Barzo P., Bauerfeind U., Beauvais R., Beer R., Belda F. J., Bellander B. -M., Belli A., Bellier R., Benali H., Benard T., Berardino M., Beretta L., Beynon C., Bilotta F., Binder H., Biqiri E., Blaabjerg M., den Boogert H., Bouzat P., Bragge P., Brazinova A., Brinck V., Brooker J., Brorsson C., Buki A., Bullinger M., Calappi E., Calvi M. R., Cameron P., Carbayo L. G., Carbonara M., Carise E., Carpenter K., Castano-Leon A. M., Causin F., Chevallard G., Chieregato A., Citerio G., Cnossen M., Coburn M., Coles J., Coles-Kemp L., Collett J., Cooper J. D., Correia M., Covic A., Curry N., Czeiter E., Czosnyka M., Dahyot-Fizelier C., Damas F., Damas P., Dawes H., De Keyser V., Della C. F., Depreitere B., Dilvesi D., Ding S., Dippel D., Dixit A., Donoghue E., Dreier J., Duliere G. -L., Eapen G., Engemann H., Ercole A., Esser P., Ezer E., Fabricius M., Feigin V. L., Feng J., Foks K., Fossi F., Francony G., Freo U., Frisvold S., Furmanov A., Gagliardo P., Galanaud D., Gantner D., Gao G., Geleijns K., George P., Ghuysen A., Giga L., Giraud B., Glocker B., Golubovic J., Gomez P. A., Grossi F., Gruen R. L., Gupta D., Haagsma J. A., Hartings J. A., Helbok R., Helseth E., Hertle D., Hoedemaekers A., Hoefer S., Horton L., Huijben J., Hutchinson P. J., Haberg A. K., Jacobs B., Jankowski S., Jarrett M., Jelaca B., Jiang J. -Y., Jones K., Kamnitsas K., Karan M., Katila A., Kaukonen M., Kerforne T., Kivisaari R., Kolias A. G., Kolumban B., Kompanje E., Kolundzija K., Kondziella D., Koskinen L. -O., Kovacs N., Lagares A., Lanyon L., Laureys S., Lecky F., Ledig C., Lefering R., Legrand V., Lei J., Levi L., Lightfoot R., Lingsma H., Loeckx D., Lozano A., MacDonald S., Maegele M., Majdan M., Major S., Manara A., Manley G., Martin D., Martin L. F., Martino C., Maruenda A., Marechal H., Masala A., Mattern J., McFadyen C., McMahon C., Melegh B., Menovsky T., Morganti-Kossmann C., Mulazzi D., Muraleedharan V., Murray L., Muhlan H., Nair N., Negru A., Nelson D., Newcombe V., Nieboer D., Noirhomme Q., Nyiradi J., Oddo M., Oldenbeuving A., Oresic M., Ortolano F., Palotie A., Parizel P. M., Patruno A., Payen J. -F., Perera N., Perlbarg V., Persona P., Peul W., Piippo-Karjalainen A., Pili F. S., Pirinen M., Ples H., Poca M. A., Pomposo I., Posti J., Puybasset L., Radoi A., Ragauskas A., Raj R., Rambadagalla M., Real R., Rehorcikova V., Rhodes J., Ripatti S., Rocka S., Roe C., Roise O., Roks G., Rosand J., Rosenfeld J., Rosenlund C., Rosenthal G., Rossaint R., Rossi S., Rueckert D., Rusnak M., Sacchi M., Sahakian B., Sahuquillo J., Sakowitz O., Sala F., Sanchez-Porras R., Sandor J., Santos E., Sasu L., Savo D., Schaffer N., Schipper I., Schlosser B., Schmidt S., Schoechl H., Schoonman G., Schou R. F., Schwendenwein E., Scholl M., Sir O., Skandsen T., Smakman L., Smeets D., Smielewski P., Sorinola A., Stamatakis E., Stanworth S., Steinbuchel N., Stevanovic A., Stevens R., Stewart W., Stocchetti N., Sundstrom N., Synnot A., Taccone F. S., Takala R., Tamas V., Tanskanen P., Taylor M. S., Te Ao B., Tenovuo O., Telgmann R., Teodorani G., Theadom A., Thomas M., Tibboel D., Tolias C., Tshibanda J. -F. L., Trapani T., Tudora C. M., Vajkoczy P., Vallance S., Valeinis E., Van der Steen G., van der Jagt M., van der Naalt J., van Dijck J. T. J. M., Van Hecke W., van Heugten C., Van Praag D., Vande Vyvere T., Van Waesberghe J., Vanhaudenhuyse A., Vargiolu A., Vega E., Velt K., Verheyden J., Vespa P. M., Vik A., Vilcinis R., Vizzino G., Vleggeert-Lankamp C., Volovici V., Voormolen D., Vulekovic P., Vamos Z., Wade D., Wang K. K. W., Wang L., Wessels L., Wildschut E., Williams G., Wilson L., Winkler M. K. L., Wolf S., Ylen P., Younsi A., Zaaroor M., Zhihui Y., Ziverte A., and Zumbo F.
Abstract: The names of themembers of the IOPSMS study Group was inverted in the original paper and is now corrected in this article.
Published: 2019

8. Distributional gaussian process layers for outlier detection in image segmentation

Author: Popescu, SG, Sharp, DJ, Cole, JH, Kamnitsas, K, Glocker, B, and Innovate UK
Subjects: cs.LG, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, stat.ML
Abstract: We propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty. This directly replaces convolving Gaussian Processes with a distance-preserving affine operator on distributions. Our experiments on brain tissue-segmentation show that the resulting architecture approaches the performance of well-established deterministic segmentation algorithms (U-Net), which has never been achieved with previous hierarchical Gaussian Processes. Moreover, by applying the same segmentation model to out-of-distribution data (i.e., images with pathology such as brain tumors), we show that our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks and reconstruction-based approaches that learn normative distributions.
Published: 2021

9. Sickle Cell Disease Severity Prediction from Percoll Gradient Images Using Graph Convolutional Networks

Author: Albarqouni, Shadi, Cardoso, M. Jorge, Dou, Qi, Kamnitsas, Konstantinos, Khanal, Bishesh, Rekik, Islem, Rieke, Nicola, Sheet, Debdoot, Tsaftaris, Sotirios, Xu, Daguang, Xu, Ziyue, Albarqouni, S ( Shadi ), Cardoso, M J ( M. Jorge ), Dou, Q ( Qi ), Kamnitsas, K ( Konstantinos ), Khanal, B ( Bishesh ), Rekik, I ( Islem ), Rieke, N ( Nicola ), Sheet, D ( Debdoot ), Tsaftaris, S ( Sotirios ), Xu, D ( Daguang ), Xu, Z ( Ziyue ), Sadafi, Ario, Makhro, Asya, Livshits, Leonid, Navab, Nassir, Bogdanova, Anna; https://orcid.org/0000-0003-0502-5381, Marr, Carsten, Albarqouni, Shadi, Cardoso, M. Jorge, Dou, Qi, Kamnitsas, Konstantinos, Khanal, Bishesh, Rekik, Islem, Rieke, Nicola, Sheet, Debdoot, Tsaftaris, Sotirios, Xu, Daguang, Xu, Ziyue, Albarqouni, S ( Shadi ), Cardoso, M J ( M. Jorge ), Dou, Q ( Qi ), Kamnitsas, K ( Konstantinos ), Khanal, B ( Bishesh ), Rekik, I ( Islem ), Rieke, N ( Nicola ), Sheet, D ( Debdoot ), Tsaftaris, S ( Sotirios ), Xu, D ( Daguang ), Xu, Z ( Ziyue ), Sadafi, Ario, Makhro, Asya, Livshits, Leonid, Navab, Nassir, Bogdanova, Anna; https://orcid.org/0000-0003-0502-5381, and Marr, Carsten
Abstract: Sickle cell disease (SCD) is a severe genetic hemoglobin disorder that results in premature destruction of red blood cells. Assessment of the severity of the disease is a challenging task in clinical routine, since the causes of broad variance in SCD manifestation despite the common genetic cause remain unclear. Identification of biomarkers that would predict the severity grade is of importance for prognosis and assessment of responsiveness of patients to therapy. Detection of the changes in red blood cell (RBC) density by means of separation of Percoll density gradients could be such a marker as it allows to resolve intercellular differences and follow the most damaged dense cells prone to destruction and vasoocclusion. Quantification and interpretation of the images obtained from the distribution of RBCs in Percoll gradients is an important prerequisite for establishment of this approach. Here, we propose a novel approach combining a graph convolutional network, a convolutional neural network, fast Fourier transform, and recursive feature elimination to predict the severity of SCD directly from a Percoll image. Two important but expensive laboratory blood test parameters are used for training the graph convolutional network. To make the model independent from such tests during prediction, these two parameters are estimated by a neural network from the Percoll image directly. On a cohort of 216 subjects, we achieve a prediction performance that is only slightly below an approach where the groundtruth laboratory measurements are used. Our proposed method is the first computational approach for the difficult task of SCD severity prediction. The two-step approach relies solely on inexpensive and simple blood analysis tools and can have a significant impact on the patients’ survival in low resource regions where access to medical instruments and doctors is limited.
Published: 2021

10. Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

Author: Maas, A, Menon, D, Adelson, P, Andelic, N, Bell, M, Belli, A, Bragge, P, Brazinova, A, Büki, A, Chesnut, R, CITERIO, GIUSEPPE, Coburn, M, Cooper, D, Crowder, A, Czeiter, E, Czosnyka, M, Diaz arrastia, R, Dreier, J, Duhaime, A, Ercole, A, Van Essen, T, Feigin, V, Gao, G, Giacino, J, Gonzalez lara, L, Gruen, R, Gupta, D, Hartings, J, Hill, S, Jiang, J, Ketharanathan, N, Kompanje, E, Lanyon, L, Laureys, S, Lecky, F, Levin, H, Lingsma, H, Maegele, M, Majdan, M, Manley, G, Marsteller, J, Mascia, L, Mcfadyen, C, Mondello, S, Newcombe, V, Palotie, A, Parizel, P, Peul, W, Piercy, J, Polinder, S, Puybasset, L, Rasmussen, T, Rossaint, R, Smielewski, P, Söderberg, J, Stanworth, S, Stein, M, Von Steinbüchel, N, Stewart, W, Steyerberg, E, Stocchetti, N, Synnot, A, Te Ao, B, Tenovuo, O, Theadom, A, Tibboel, D, Videtta, W, Wang, K, Williams, W, Wilson, L, Yaffe, K, Adams, H, Agnoletti, V, Allanson, J, Amrein, K, Andaluz, N, Anke, A, Antoni, A, Van As, A, Audibert, G, Azaševac, A, Azouvi, P, Azzolini, M, Baciu, C, Badenes, R, Barlow, K, Bartels, R, Bauerfeind, U, Beauchamp, M, Beer, D, Beer, R, Belda, F, Bellander, B, Bellier, R, Benali, H, Benard, T, Beqiri, V, Beretta, L, Bernard, F, Bertolini, G, Bilotta, F, Blaabjerg, M, Den Boogert, H, Boutis, K, Bouzat, P, Brooks, B, Brorsson, C, Bullinger, M, Burns, E, Calappi, E, Cameron, P, Carise, E, Castaño león, A, Causin, F, Chevallard, G, Chieregato, A, Christie, B, Cnossen, M, Coles, J, Collett, J, Della Corte, F, Craig, W, Csato, G, Csomos, A, Curry, N, Dahyot fizelier, C, Dawes, H, Dematteo, C, Depreitere, B, Dewey, D, Van Dijck, J, Đilvesi, Đ, Dippel, D, Dizdarevic, K, Donoghue, E, Duek, O, Dulière, G, Dzeko, A, Eapen, G, Emery, C, English, S, Esser, P, Ezer, E, Fabricius, M, Feng, J, Fergusson, D, Figaji, A, Fleming, J, Foks, K, Francony, G, Freedman, S, Freo, U, Frisvold, S, Gagnon, I, Galanaud, D, Gantner, D, Giraud, B, Glocker, B, Golubovic, J, Gómez López, P, Gordon, W, Gradisek, P, Gravel, J, Griesdale, D, Grossi, F, Haagsma, J, Håberg, A, Haitsma, I, Van Hecke, W, Helbok, R, Helseth, E, Van Heugten, C, Hoedemaekers, C, Höfer, S, Horton, L, Hui, J, Huijben, J, Hutchinson, P, Jacobs, B, Van Der Jagt, M, Jankowski, S, Janssens, K, Jelaca, B, Jones, K, Kamnitsas, K, Kaps, R, Karan, M, Katila, A, Kaukonen, K, De Keyser, V, Kivisaari, R, Kolias, A, Kolumbán, B, Kolundžija, K, Kondziella, D, Koskinen, L, Kovács, N, Kramer, A, Kutsogiannis, D, Kyprianou, T, Lagares, A, Lamontagne, F, Latini, R, Lauzier, F, Lazar, I, Ledig, C, Lefering, R, Legrand, V, Levi, L, Lightfoot, R, Lozano, A, Macdonald, S, Major, S, Manara, A, Manhes, P, Maréchal, H, Martino, C, Masala, A, Masson, S, Mattern, J, Mcfadyen, B, Mcmahon, C, Meade, M, Melegh, B, Menovsky, T, Moore, L, Morgado Correia, M, Morganti kossmann, M, Muehlan, H, Mukherjee, P, Murray, L, Van Der Naalt, J, Negru, A, Nelson, D, Nieboer, D, Noirhomme, Q, Nyirádi, J, Oddo, M, Okonkwo, D, Oldenbeuving, A, Ortolano, F, Osmond, M, Payen, J, Perlbarg, V, Persona, P, Pichon, N, Piippo karjalainen, A, Pili floury, S, Pirinen, M, Ple, H, Poca, M, Posti, J, Van Praag, D, Ptito, A, Radoi, A, Ragauskas, A, Raj, R, Real, R, Reed, N, Rhodes, J, Robertson, C, Rocka, S, Røe, C, Røise, O, Roks, G, Rosand, J, Rosenfeld, J, Rosenlund, C, Rosenthal, G, Rossi, S, Rueckert, D, De Ruiter, G, Sacchi, M, Sahakian, B, Sahuquillo, J, Sakowitz, O, Salvato, G, Sánchez porras, R, Sándor, J, Sangha, G, Schäfer, N, Schmidt, S, Schneider, K, Schnyer, D, Schöhl, H, Schoonman, G, Schou, R, Sir, Ö, Skandsen, T, Smeets, D, Sorinola, A, Stamatakis, E, Stevanovic, A, Stevens, R, Sundström, N, Taccone, F, Takala, R, Tanskanen, P, Taylor, M, Telgmann, R, Temkin, N, Teodorani, G, Thomas, M, Tolias, C, Trapani, T, Turgeon, A, Vajkoczy, P, Valadka, A, Valeinis, E, Vallance, S, Vámos, Z, VARGIOLU, ALESSIA, Vega, E, Verheyden, J, Vik, A, Vilcinis, R, Vleggeert lankamp, C, Vogt, L, Volovici, V, Voormolen, D, Vulekovic, P, Vande Vyvere, T, Van Waesberghe, J, Wessels, L, Wildschut, E, Williams, G, Winkler, M, Wolf, S, Wood, G, Xirouchaki, N, Younsi, A, Zaaroor, M, Zelinkova, V, Zemek, R, Zumbo, F, Citerio, G, Vargiolu, A, Zumbo, F., Maas, Andrew I R, Menon, David K, Adelson, P David, Andelic, Nada, Bell, Michael J, Belli, Antonio, Bragge, Peter, Brazinova, Alexandra, Büki, Andrá, Chesnut, Randall M, Citerio, Giuseppe, Coburn, Mark, Cooper, D Jamie, Crowder, A Tamara, Czeiter, Endre, Czosnyka, Marek, Diaz-Arrastia, Ramon, Dreier, Jens P, Duhaime, Ann-Christine, Ercole, Ari, van Essen, Thomas A, Feigin, Valery L, Gao, Guoyi, Giacino, Joseph, Gonzalez-Lara, Laura E, Gruen, Russell L, Gupta, Deepak, Hartings, Jed A, Hill, Sean, Jiang, Ji-yao, Ketharanathan, Naomi, Kompanje, Erwin J O, Lanyon, Linda, Laureys, Steven, Lecky, Fiona, Levin, Harvey, Lingsma, Hester F, Maegele, Marc, Majdan, Marek, Manley, Geoffrey, Marsteller, Jill, Mascia, Luciana, Mcfadyen, Charle, Mondello, Stefania, Newcombe, Virginia, Palotie, Aarno, Parizel, Paul M, Peul, Wilco, Piercy, Jame, Polinder, Suzanne, Puybasset, Loui, Rasmussen, Todd E, Rossaint, Rolf, Smielewski, Peter, Söderberg, Jeannette, Stanworth, Simon J, Stein, Murray B, von Steinbüchel, Nicole, Stewart, William, Steyerberg, Ewout W, Stocchetti, Nino, Synnot, Anneliese, Te Ao, Braden, Tenovuo, Olli, Theadom, Alice, Tibboel, Dick, Videtta, Walter, Wang, Kevin K W, Williams, W Huw, Wilson, Lindsay, Yaffe, Kristine, InTBIR Participants, Investigator, Beretta, Luigi, InTBIR Participants Investigators, Menon, David [0000-0002-3228-9692], Czosnyka, Marek [0000-0003-2446-8006], Ercole, Ari [0000-0001-8350-8093], Newcombe, Virginia [0000-0001-6044-9035], Smielewski, Peter [0000-0001-5096-3938], Apollo - University of Cambridge Repository, Maas A.I.R., Menon D.K., David Adelson P.D., Andelic N., Bell M.J., Belli A., Bragge P., Brazinova A., Buki A., Chesnut R.M., Citerio G., Coburn M., Jamie Cooper D., Tamara Crowder A., Czeiter E., Czosnyka M., Diaz-Arrastia R., Dreier J.P., Duhaime A.-C., Ercole A., van Essen T.A., Feigin V.L., Gao G., Giacino J., Gonzalez-Lara L.E., Gruen R.L., Gupta D., Hartings J.A., Hill S., Jiang J.-Y., Ketharanathan N., Kompanje E.J.O., Lanyon L., Laureys S., Lecky F., Levin H., Lingsma H.F., Maegele M., Majdan M., Manley G., Marsteller J., Mascia L., McFadyen C., Mondello S., Newcombe V., Palotie A., Parizel P.M., Peul W., Piercy J., Polinder S., Puybasset L., Rasmussen T.E., Rossaint R., Smielewski P., Soderberg J., Stanworth S.J., Stein M.B., von Steinbuchel N., Stewart W., Steyerberg E.W., Stocchetti N., Synnot A., Te Ao B., Tenovuo O., Theadom A., Tibboel D., Videtta W., Wang K.K.W., Huw Williams W., Wilson L., Yaffe K., Adams H., Allanson J., Coles J., Hutchinson P.J., Kolias A.G., Sahakian B.J., Stamatakis E., Williams G., Agnoletti V., Martino C., Masala A., Teodorani G., Zumbo F., Amrein K., Ezer E., Kolumban B., Kovacs N., Melegh B., Nyiradi J., Sorinola A., Vamos Z., Andaluz N., Anke A., Frisvold S.K., Antoni A., van As A.B., Figaji A., Audibert G., Azasevac A., Dilvesi D., Golubovic J., Jelaca B., Karan M., Kolundzija K., Negru A., Vulekovic P., Azouvi P., Azzolini M.L., Beretta L., Baciu C., Beqiri V., Chevallard G., Chieregato A., Sacchi M., Badenes R., Belda F.J., Bilotta F., Lozano A., Barlow K.M., Schneider K.J., Bartels R., den Boogert H., Hoedemaekers C., Sir O., Bauerfeind U., Lefering R., Schafer N., Beauchamp M., Gravel J., Beer D., Beer R., Helbok R., Hofer S., Bellander B.-M., Nelson D., Bellier R., Benard T., Carise E., Dahyot-Fizelier C., Giraud B., Benali H., Bernard F., Bertolini G., Masson S., Blaabjerg M., Rosenlund C., Schou R.F., Boutis K., Bouzat P., Francony G., Manhes P., Payen J.-F., Brooks B., Dewey D., Emery C.A., Freedman S., Kramer A., Brorsson C., Koskinen L.-O., Sundstrom N., Bullinger M., Burns E., Calappi E., Ortolano F., Cameron P., Castano-Leon A.M., Gomez Lopez P.A., Lagares A., Causin F., Freo U., Persona P., Rossi S., Christie B., Cnossen M., Dippel D., Foks K., Haagsma J.A., Haitsma I., Huijben J.A., van der Jagt M., Nieboer D., Volovici V., Voormolen D.C., Collett J., Dawes H., Esser P., van Heugten C., Della Corte F., Grossi F., Craig W., Csato G., Csomos A., Curry N., Dematteo C., Meade M., Depreitere B., van Dijck J., de Ruiter G.C.W., Vleggeert-Lankamp C., Dizdarevic K., Donoghue E., Gantner D., Murray L., Trapani T., Vallance S., Duek O., Lazar I., Duliere G.-L., Marechal H., Dzeko A., Eapen G., Jankowski S., English S., Fergusson D., Osmond M., Fabricius M., Kondziella D., Feng J., Hui J., Fleming J., Latini R., Gagnon I., Ptito A., Galanaud D., Glocker B., Kamnitsas K., Ledig C., Rueckert D., Gordon W.A., Gradisek P., Griesdale D., Haberg A.K., van Hecke W., Smeets D., Verheyden J., Vyvere T.V., Helseth E., Roe C., Roise O., Horton L., Jacobs B., van der Naalt J., Janssens K., De Keyser V., Menovsky T., Van Praag D., Jones K.M., Kaps R., Katila A., Posti J., Takala R., Kaukonen K.-M., Kivisaari R., Piippo-Karjalainen A., Raj R., Tanskanen P., Kutsogiannis D., Kyprianou T., Lamontagne F., Lauzier F., Moore L., Turgeon A., Legrand V., Levi L., Zaaroor M., Lightfoot R., Macdonald S., Major S., Vajkoczy P., Wessels L., Winkler M.K.L., Wolf S., Manara A., Thomas M., Mattern J., Sakowitz O., Vogt L., Younsi A., McFadyen B., McMahon C., Correia M.M., Morganti-Kossmann M.C., Rosenfeld J.V., Muehlan H., Schmidt S., Mukherjee P., Noirhomme Q., Oddo M., Okonkwo D.O., Oldenbeuving A.W., Roks G., Schoonman G.G., Perlbarg V., Pichon N., Pili-Floury S., Pirinen M., Ples H., Poca M.A., Radoi A., Sahuquillo J., Ragauskas A., Rocka S., Real R.G.L., Telgmann R., Reed N., Rhodes J., Robertson C., Rosand J., Rosenthal G., Salvato G., Sanchez-Porras R., Sandor J., Sangha G., Schnyer D., Schohl H., Skandsen T., Stevanovic A., van Waesberghe J.V., Stevens R.D., Taccone F.S., Taylor M.S., Zelinkova V., Temkin N., Tolias C.M., Valadka A.B., Valeinis E., Vargiolu A., Vega E., Vik A., Vilcinis R., Wildschut E., Wood G., Xirouchaki N., Zemek R., Maas, A, Menon, D, Adelson, P, Andelic, N, Bell, M, Belli, A, Bragge, P, Brazinova, A, Büki, A, Chesnut, R, Citerio, G, Coburn, M, Cooper, D, Crowder, A, Czeiter, E, Czosnyka, M, Diaz arrastia, R, Dreier, J, Duhaime, A, Ercole, A, Van Essen, T, Feigin, V, Gao, G, Giacino, J, Gonzalez lara, L, Gruen, R, Gupta, D, Hartings, J, Hill, S, Jiang, J, Ketharanathan, N, Kompanje, E, Lanyon, L, Laureys, S, Lecky, F, Levin, H, Lingsma, H, Maegele, M, Majdan, M, Manley, G, Marsteller, J, Mascia, L, Mcfadyen, C, Mondello, S, Newcombe, V, Palotie, A, Parizel, P, Peul, W, Piercy, J, Polinder, S, Puybasset, L, Rasmussen, T, Rossaint, R, Smielewski, P, Söderberg, J, Stanworth, S, Stein, M, Von Steinbüchel, N, Stewart, W, Steyerberg, E, Stocchetti, N, Synnot, A, Te Ao, B, Tenovuo, O, Theadom, A, Tibboel, D, Videtta, W, Wang, K, Williams, W, Wilson, L, Yaffe, K, Adams, H, Agnoletti, V, Allanson, J, Amrein, K, Andaluz, N, Anke, A, Antoni, A, Van As, A, Audibert, G, Azaševac, A, Azouvi, P, Azzolini, M, Baciu, C, Badenes, R, Barlow, K, Bartels, R, Bauerfeind, U, Beauchamp, M, Beer, D, Beer, R, Belda, F, Bellander, B, Bellier, R, Benali, H, Benard, T, Beqiri, V, Beretta, L, Bernard, F, Bertolini, G, Bilotta, F, Blaabjerg, M, Den Boogert, H, Boutis, K, Bouzat, P, Brooks, B, Brorsson, C, Bullinger, M, Burns, E, Calappi, E, Cameron, P, Carise, E, Castaño león, A, Causin, F, Chevallard, G, Chieregato, A, Christie, B, Cnossen, M, Coles, J, Collett, J, Della Corte, F, Craig, W, Csato, G, Csomos, A, Curry, N, Dahyot fizelier, C, Dawes, H, Dematteo, C, Depreitere, B, Dewey, D, Van Dijck, J, Đilvesi, Đ, Dippel, D, Dizdarevic, K, Donoghue, E, Duek, O, Dulière, G, Dzeko, A, Eapen, G, Emery, C, English, S, Esser, P, Ezer, E, Fabricius, M, Feng, J, Fergusson, D, Figaji, A, Fleming, J, Foks, K, Francony, G, Freedman, S, Freo, U, Frisvold, S, Gagnon, I, Galanaud, D, Gantner, D, Giraud, B, Glocker, B, Golubovic, J, Gómez López, P, Gordon, W, Gradisek, P, Gravel, J, Griesdale, D, Grossi, F, Haagsma, J, Håberg, A, Haitsma, I, Van Hecke, W, Helbok, R, Helseth, E, Van Heugten, C, Hoedemaekers, C, Höfer, S, Horton, L, Hui, J, Huijben, J, Hutchinson, P, Jacobs, B, Van Der Jagt, M, Jankowski, S, Janssens, K, Jelaca, B, Jones, K, Kamnitsas, K, Kaps, R, Karan, M, Katila, A, Kaukonen, K, De Keyser, V, Kivisaari, R, Kolias, A, Kolumbán, B, Kolundžija, K, Kondziella, D, Koskinen, L, Kovács, N, Kramer, A, Kutsogiannis, D, Kyprianou, T, Lagares, A, Lamontagne, F, Latini, R, Lauzier, F, Lazar, I, Ledig, C, Lefering, R, Legrand, V, Levi, L, Lightfoot, R, Lozano, A, Macdonald, S, Major, S, Manara, A, Manhes, P, Maréchal, H, Martino, C, Masala, A, Masson, S, Mattern, J, Mcfadyen, B, Mcmahon, C, Meade, M, Melegh, B, Menovsky, T, Moore, L, Morgado Correia, M, Morganti kossmann, M, Muehlan, H, Mukherjee, P, Murray, L, Van Der Naalt, J, Negru, A, Nelson, D, Nieboer, D, Noirhomme, Q, Nyirádi, J, Oddo, M, Okonkwo, D, Oldenbeuving, A, Ortolano, F, Osmond, M, Payen, J, Perlbarg, V, Persona, P, Pichon, N, Piippo karjalainen, A, Pili floury, S, Pirinen, M, Ple, H, Poca, M, Posti, J, Van Praag, D, Ptito, A, Radoi, A, Ragauskas, A, Raj, R, Real, R, Reed, N, Rhodes, J, Robertson, C, Rocka, S, Røe, C, Røise, O, Roks, G, Rosand, J, Rosenfeld, J, Rosenlund, C, Rosenthal, G, Rossi, S, Rueckert, D, De Ruiter, G, Sacchi, M, Sahakian, B, Sahuquillo, J, Sakowitz, O, Salvato, G, Sánchez porras, R, Sándor, J, Sangha, G, Schäfer, N, Schmidt, S, Schneider, K, Schnyer, D, Schöhl, H, Schoonman, G, Schou, R, Sir, Ö, Skandsen, T, Smeets, D, Sorinola, A, Stamatakis, E, Stevanovic, A, Stevens, R, Sundström, N, Taccone, F, Takala, R, Tanskanen, P, Taylor, M, Telgmann, R, Temkin, N, Teodorani, G, Thomas, M, Tolias, C, Trapani, T, Turgeon, A, Vajkoczy, P, Valadka, A, Valeinis, E, Vallance, S, Vámos, Z, Vargiolu, A, Vega, E, Verheyden, J, Vik, A, Vilcinis, R, Vleggeert lankamp, C, Vogt, L, Volovici, V, Voormolen, D, Vulekovic, P, Vande Vyvere, T, Van Waesberghe, J, Wessels, L, Wildschut, E, Williams, G, Winkler, M, Wolf, S, Wood, G, Xirouchaki, N, Younsi, A, Zaaroor, M, Zelinkova, V, Zemek, R, Zumbo, F, Pediatric Surgery, Intensive Care, and Public Health
Subjects: medicine.medical_specialty, EVIDENCE-BASED MEDICINE, Treatment outcome, Poison control, Other Research Radboud Institute for Molecular Life Sciences [Radboudumc 0], EMERGENCY-DEPARTMENT VISITS, Review, PLACEBO-CONTROLLED TRIAL, Middle income country, Healthcare improvement science Radboud Institute for Health Sciences [Radboudumc 18], 03 medical and health sciences, 0302 clinical medicine, Intensive care, Brain Injuries, Traumatic, Journal Article, medicine, traumatic barin injury, Humans, 030212 general & internal medicine, Clinical care, Neurologic disease, Psychiatry, DIAGNOSTIC MANAGEMENT STRATEGIES, business.industry, RANDOMIZED CONTROLLED-TRIAL, ACUTE SUBDURAL-HEMATOMA, SEVERE HEAD-INJURY, ROAD TRAFFIC INJURIES, brain injury, Hospital care, 3. Good health, Reconstructive and regenerative medicine Radboud Institute for Health Sciences [Radboudumc 10], Brain Injuries, Health care cost, PATIENT-REPORTED OUTCOMES, Human medicine, Neurology (clinical), business, Humanities, 030217 neurology & neurosurgery, GLASGOW COMA SCALE
Abstract: Executive summary A concerted effort to tackle the global health problem posed by traumatic brain injury (TBI) is long overdue. TBI is a public health challenge of vast, but insufficiently recognised, proportions. Worldwide, more than 50 million people have a TBI each year, and it is estimated that about half the world’s population will have one or more TBIs over their lifetime. TBI is the leading cause of mortality in young adults and a major cause of death and disability across all ages in all countries, with a disproportionate burden of disability and death occurring in low-income and middle-income countries (LMICs). It has been estimated that TBI costs the global economy approximately $US400 billion annually. Deficiencies in prevention, care, and research urgently need to be addressed to reduce the huge burden and societal costs of TBI. This Commission highlights priorities and provides expert recommendations for all stakeholders— policy makers, funders, health-care professionals, researchers, and patient representatives—on clinical and research strategies to reduce this growing public health problem and improve the lives of people with TBI. The epidemiology of TBI is changing: in high-income countries, the number of elderly people with TBI is increasing, mainly due to falls, while in LMICs, the burden of TBI from road traffic incidents is increasing. Data on the frequency of TBI and TBI-related deaths and on the economic impact of brain trauma are often incomplete and vary between countries. Improved, accurate epidemiological monitoring and robust healtheconomic data collection are needed to inform healthcare policy and prevention programmes. Highly developed and coordinated systems of care are crucial for management of patients with TBI. However, in practice, implementation of such frameworks varies greatly and disconnects exist in the chain of care. Optimisation of systems of care should be high on the policy agenda and could yield substantial gains in terms of both patient outcomes and costs to society. TBI is a complex condition, and strong evidence to support treatment guidelines and recommendations is scarce. Most multicentre clinical trials of medical and surgical interventions have failed to show efficacy, despite promising preclinical results. At the bedside, treatment strategies are generally based on guidelines that promote a one-size-fits-all approach and are insufficiently targeted to the needs of individual patients. Attempts to individualise treatment are challenging owing to the diversity of TBI, and are hampered by the use of simplistic methods to characterise its initial type and severity. Advances in genomics, blood biomarkers, magnetic resonance imaging (MRI), and pathophysiological monitoring, combined with informatics to integrate data from multiple sources, offer new research avenues to improve disease characterisation and monitoring of disease evolution. These tools can also aid understanding of disease mechanisms and facilitate targeted treatment strategies for individual patients. Individualised management in the postacute phase and evaluation of the effectiveness of treatment and care processes depend on accurate quantification of outcomes. In practice, however, the use of simplistic methods hinders efforts to quantify outcomes after TBI of all severities. Development and validation of multidimensional approaches will be essential to improve measurement of clinical outcomes, for both research and patient care. In particular, we need to find better ways to characterise the currently under-recognised risk of long-term disabling sequelae in patients with relatively mild injuries. Prognostic models are important to help clinicians to provide reliable information to patients and relatives, and to facilitate comparative audit of care between centres and countries. There is an urgent need for further development, validation, and implementation of prognostic models in TBI, particularly for less severe TBI. This multitude of challenges in TBI—encompassing systems of care, clinical management, and research strategy—demands novel approaches to the generation of new evidence and its implementation in clinical practice. Comparative effectiveness research (CER) offers opportunities to capitalise on the diversity of TBI and systems of care and enables assessment of therapies in real-world conditions; high-quality CER studies can provide strong evidence to support guideline recommendations. The global challenges posed by TBI necessitate global collaborations and a change in research culture to endorse broad data sharing. This Commission covers a range of topics that need to be addressed to confront the global burden of TBI and reduce its effects on individuals and society: epidemiology (section 1); health economics (section 2); prevention (section 3); systems of care (section 4); clinical management (section 5); characterisation of TBI (section 6); outcome assessment (section 7); prognosis (section 8); and new directions for acquiring and implementing evidence (section 9). Table 1 summarises key messages from the Commission and provides recommendations to advance clinical care and research in TBI. We must increase awareness of the scale of the challenge posed by TBI. If we are to tackle the individual and societal burden of TBI, these efforts need to go beyond a clinical and research audience and address the public, politicians, and other stakeholders. We need to develop and implement policies for better prevention and systems of care in order to improve outcomes for individuals with TBI. We also need a commitment to substantial long-term investment in TBI research across a range of disciplines to determine best practice and facilitate individualised management strategies. A combination of innovative research methods and global collaboration, and ways to effectively translate progress in basic and clinical research into clinical practice and public health policy, will be vital for progress in the field.
Published: 2017

11. A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling

Author: Fetit, AE, Alansary, A, Cordero-Grande, L, Cupitt, J, Davidson, AB, Edwards, AD, Hajnal, JV, Hughes, EJ, Kamnitsas, K, Kyriakopoulou, V, Makropoulos, A, Patkee, PA, Price, AN, Rutherford, MA, Rueckert, D, Arbel, T, Ayed, IB, Bruijne, MD, Descoteaux, M, Lombaert, H, and Pal, C
Abstract: We developed an automated system based on deep neural networks for fast and sensitive 3D image segmentation of cortical gray matter from fetal brain MRI. The lack of extensive/publicly available annotations presented a key challenge, as large amounts of labeled data are typically required for training sensitive models with deep learning. To address this, we: (i) generated preliminary tissue labels using the {\em Draw-EM} algorithm, which uses Expectation-Maximization and was originally designed for tissue segmentation in the neonatal domain; and (ii) employed a human-in-the-loop approach, whereby an expert fetal imaging annotator assessed and refined the performance of the model. By using a hybrid approach that combined automatically generated labels with manual refinements by an expert, we amplified the utility of ground truth annotations while immensely reducing their cost (283 slices). The deep learning system was developed, refined, and validated on 249 3D T2-weighted scans obtained from the {\em Developing Human Connectome Project}’s fetal cohort, acquired at 3T. Analysis of the system showed that it is invariant to gestational age at scan, as it generalized well to a wide age range (21 � 38 weeks) despite variations in cortical morphology and intensity across the fetal distribution. It was also found to be invariant to intensities in regions surrounding the brain (amniotic fluid), which often present a major obstacle to the processing of neuroimaging data in the fetal domain.
Published: 2020

12. Explainable anatomical shape analysis through deep hierarchical generative models

Author: Biffi, C, Doumou, G, Duan, J, Prasad, SK, Cook, SA, O Regan, DP, Rueckert, D, Cerrolaza, JJ, Tarroni, G, Bai, W, De Marvao, A, Oktay, O, Ledig, C, Le Folgoc, L, and Kamnitsas, K
Subjects: Nuclear Medicine & Medical Imaging, cs.LG, eess.IV, 08 Information and Computing Sciences, cs.CV, 09 Engineering
Abstract: Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating highthroughput analysis of normal anatomy and pathology in largescale studies of volumetric imaging.
Published: 2020

13. Brain death and postmortem organ donation: report of a questionnaire from the CENTER-TBI study

Author: Van Veen E., Van Der Jagt M., Cnossen M. C., Maas A. I. R., De Beaufort I. D., Menon D. K., Citerio G., Stocchetti N., Rietdijk W. J. R., Van Dijck J. T. J. M., Kompanje E. J. O., Ackerlund C., Adams H., Agnoletti V., Allanson J., Amrein K., Andaluz N., Andelic N., Andreassen L., Antun A., Anke A., Antoni A., Ardon H., Audibert G., Auslands K., Azouvi P., Azzolini M. L., Baciu C., Badenes R., Bartels R., Barzo P., Bauerfeind U., Beauvais R., Beer R., Francisco J. B., Bellander B. -M., Belli A., Bellier R., Benali H., Benard T., Berardino M., Beretta L., Beynon C., Bilotta F., Binder H., Biqiri E., Blaabjerg M., Den Boogert H., Bouzat P., Bragge P., Brazinova A., Brinck V., Brooker J., Brorsson C., Buki A., Bullinger M., Calappi E., Calvi M. R., Cameron P., Lozano G. C., Carbonara M., Carise E., Carpenter K., Castano-Leon A. M., Causin F., Chevallard G., Chieregato A., Coburn M., Coles J., Coles-Kemp L., Collett J., Cooper J. D., Correia M., Covic A., Curry N., Czeiter E., Czosnyka M., Dahyot-Fizelier C., Damas F., Damas P., Dawes H., De Keyser V., Della Corte F., Depreitere B., De Ruiter Godard C. W., Dilvesi D., Ding S., Dippel D., Dixit A., Donoghue E., Dreier J., Duliere G. -L., Eapen G., Engemann H., Ercole A., Esser P., Ezer E., Fabricius M., Feigin V. L., Feng J., Foks K., Fossi F., Francony G., Freo U., Frisvold S., Furmanov A., Gagliardo P., Galanaud D., Gantner D., Gao G., Geleijns K., George P., Ghuysen A., Giga L., Giraud B., Glocker B., Golubovic J., Gomez P. A., Grossi F., Gruen R. L., Gupta D., Haagsma J. A., Haitsma I., Hartings J. A., Helbok R., Helseth E., Hertle D., Hoedemaekers A., Hoefer S., Horton L., Huijben J., Hutchinson P. J., Haberg A. K., Jacobs B., Jankowski S., Jarrett M., Jelaca B., Jiang J. -Y., Jones K., Kamnitsas K., Karan M., Katila A., Kaukonen M., Kerforne T., Kivisaari R., Kolias A. G., Kolumban B., Kolundzija K., Kondziella D., Koskinen L. -O., Kovacs N., Lagares A., Lanyon L., Laureys S., Lecky F., Ledig C., Lefering R., Legrand V., Lei J., Levi L., Lightfoot R., Lingsma H., Loeckx D., Lozano A., MacDonald S., Maegele M., Majdan M., Major S., Manara A., Manley G., Didier M., Martin L. F., Martino C., Maruenda A., Marechal H., Masala A., Mattern J., McFadyen C., McMahon C., Melegh B., Menovsky T., Morganti-Kossmann C., Mulazzi D., Muraleedharan V., Murray L., Muhlan H., Nair N., Negru A., Nelson D., Newcombe V., Nieboer D., Noirhomme Q., Nyiradi J., Oddo M., Oldenbeuving A., Oresic M., Ortolano F., Palotie A., Parizel P. M., Patruno A., Payen J. -F., Perera N., Perlbarg V., Persona P., Peul W., Piippo-Karjalainen A., Floury S. P., Pirinen M., Ples H., Poca M. A., Polinder S., Pomposo I., Posti J., Puybasset L., Radoi A., Ragauskas A., Raj R., Rambadagalla M., Real R., Rehorcikova V., Rhodes J., Ripatti S., Rocka S., Roe C., Roise O., Roks G., Rosand J., Rosenfeld J., Rosenlund C., Rosenthal G., Rossaint R., Rossi S., Rueckert D., Rusnak M., Sacchi M., Sahakian B., Sahuquillo J., Sakowitz O., Sala F., Sanchez-Porras R., Sandor J., Santos E., Sasu L., Savo D., Schaffer N., Schipper I., Schlosser B., Schmidt S., Schoechl H., Schoonman G., Schou R. F., Schwendenwein E., Scholl M., Sir O., Skandsen T., Smakman L., Smeets D., Smielewski P., Sorinola A., Stamatakis E., Stanworth S., Steinbuchel N., Stevanovic A., Stevens R., Stewart W., Steyerberg E. W., Sundstrom N., Synnot A., Taccone F. S., Takala R., Tamas V., Tanskanen P., Taylor M. S., Te Ao B., Tenovuo O., Telgmann R., Teodorani G., Theadom A., Thomas M., Tibboel D., Tolias C., Tshibanda J. -F. L., Trapani T., Tudora C. M., Vajkoczy P., Vallance S., Valeinis E., Van Der Steen G., Van Der Naalt J., Van Essen T. A., Van Hecke W., Van Heugten C., Van Praag D., Vyvere T. V., Van Waesberghe J., Vanhaudenhuyse A., Vargiolu A., Vega E., Velt K., Verheyden J., Vespa P. M., Vik A., Vilcinis R., Vizzino G., Vleggeert-Lankamp C., Volovici V., Voormolen D., Vulekovic P., Vamos Z., Wade D., Wang K. K. W., Wang L., Wessels L., Wildschut E., Williams G., Wilson L., Winkler M. K. L., Wolf S., Ylen P., Younsi A., Zaaroor M., Zhihui Y., Ziverte A., Zumbo F., Intensive Care, Public Health, van Veen, Ernest, van der Jagt, Mathieu, Cnossen, Maryse C., Maas, Andrew I R, de Beaufort, Inez D., Menon, David K., Citerio, Giuseppe, Stocchetti, Nino, Rietdijk, Wim J R, van Dijck, Jeroen T J M, Kompanje, Erwin J O (CENTER-TBI investigators and participants), Beretta, Luigi, Apollo - University of Cambridge Repository, Ragauskas, Arminas, Rocka, Saulius, Vilcinis, Rimantas, „Springer' grupė, Neurokirurgian yksikkö, Clinicum, Van Veen E., Van Der Jagt M., Cnossen M.C., Maas A.I.R., De Beaufort I.D., Menon D.K., Citerio G., Stocchetti N., Rietdijk W.J.R., Van Dijck J.T.J.M., Kompanje E.J.O., Ackerlund C., Adams H., Agnoletti V., Allanson J., Amrein K., Andaluz N., Andelic N., Andreassen L., Antun A., Anke A., Antoni A., Ardon H., Audibert G., Auslands K., Azouvi P., Azzolini M.L., Baciu C., Badenes R., Bartels R., Barzo P., Bauerfeind U., Beauvais R., Beer R., Francisco J.B., Bellander B.-M., Belli A., Bellier R., Benali H., Benard T., Berardino M., Beretta L., Beynon C., Bilotta F., Binder H., Biqiri E., Blaabjerg M., Den Boogert H., Bouzat P., Bragge P., Brazinova A., Brinck V., Brooker J., Brorsson C., Buki A., Bullinger M., Calappi E., Calvi M.R., Cameron P., Lozano G.C., Carbonara M., Carise E., Carpenter K., Castano-Leon A.M., Causin F., Chevallard G., Chieregato A., Coburn M., Coles J., Coles-Kemp L., Collett J., Cooper J.D., Correia M., Covic A., Curry N., Czeiter E., Czosnyka M., Dahyot-Fizelier C., Damas F., Damas P., Dawes H., De Keyser V., Della Corte F., Depreitere B., De Ruiter Godard C.W., Dilvesi D., Ding S., Dippel D., Dixit A., Donoghue E., Dreier J., Duliere G.-L., Eapen G., Engemann H., Ercole A., Esser P., Ezer E., Fabricius M., Feigin V.L., Feng J., Foks K., Fossi F., Francony G., Freo U., Frisvold S., Furmanov A., Gagliardo P., Galanaud D., Gantner D., Gao G., Geleijns K., George P., Ghuysen A., Giga L., Giraud B., Glocker B., Golubovic J., Gomez P.A., Grossi F., Gruen R.L., Gupta D., Haagsma J.A., Haitsma I., Hartings J.A., Helbok R., Helseth E., Hertle D., Hoedemaekers A., Hoefer S., Horton L., Huijben J., Hutchinson P.J., Haberg A.K., Jacobs B., Jankowski S., Jarrett M., Jelaca B., Jiang J.-Y., Jones K., Kamnitsas K., Karan M., Katila A., Kaukonen M., Kerforne T., Kivisaari R., Kolias A.G., Kolumban B., Kolundzija K., Kondziella D., Koskinen L.-O., Kovacs N., Lagares A., Lanyon L., Laureys S., Lecky F., Ledig C., Lefering R., Legrand V., Lei J., Levi L., Lightfoot R., Lingsma H., Loeckx D., Lozano A., MacDonald S., Maegele M., Majdan M., Major S., Manara A., Manley G., Didier M., Martin L.F., Martino C., Maruenda A., Marechal H., Masala A., Mattern J., McFadyen C., McMahon C., Melegh B., Menovsky T., Morganti-Kossmann C., Mulazzi D., Muraleedharan V., Murray L., Muhlan H., Nair N., Negru A., Nelson D., Newcombe V., Nieboer D., Noirhomme Q., Nyiradi J., Oddo M., Oldenbeuving A., Oresic M., Ortolano F., Palotie A., Parizel P.M., Patruno A., Payen J.-F., Perera N., Perlbarg V., Persona P., Peul W., Piippo-Karjalainen A., Floury S.P., Pirinen M., Ples H., Poca M.A., Polinder S., Pomposo I., Posti J., Puybasset L., Radoi A., Ragauskas A., Raj R., Rambadagalla M., Real R., Rehorcikova V., Rhodes J., Ripatti S., Rocka S., Roe C., Roise O., Roks G., Rosand J., Rosenfeld J., Rosenlund C., Rosenthal G., Rossaint R., Rossi S., Rueckert D., Rusnak M., Sacchi M., Sahakian B., Sahuquillo J., Sakowitz O., Sala F., Sanchez-Porras R., Sandor J., Santos E., Sasu L., Savo D., Schaffer N., Schipper I., Schlosser B., Schmidt S., Schoechl H., Schoonman G., Schou R.F., Schwendenwein E., Scholl M., Sir O., Skandsen T., Smakman L., Smeets D., Smielewski P., Sorinola A., Stamatakis E., Stanworth S., Steinbuchel N., Stevanovic A., Stevens R., Stewart W., Steyerberg E.W., Sundstrom N., Synnot A., Taccone F.S., Takala R., Tamas V., Tanskanen P., Taylor M.S., Te Ao B., Tenovuo O., Telgmann R., Teodorani G., Theadom A., Thomas M., Tibboel D., Tolias C., Tshibanda J.-F.L., Trapani T., Tudora C.M., Vajkoczy P., Vallance S., Valeinis E., Van Der Steen G., Van Der Naalt J., Van Essen T.A., Van Hecke W., Van Heugten C., Van Praag D., Vyvere T.V., Van Waesberghe J., Vanhaudenhuyse A., Vargiolu A., Vega E., Velt K., Verheyden J., Vespa P.M., Vik A., Vilcinis R., Vizzino G., Vleggeert-Lankamp C., Volovici V., Voormolen D., Vulekovic P., Vamos Z., Wade D., Wang K.K.W., Wang L., Wessels L., Wildschut E., Williams G., Wilson L., Winkler M.K.L., Wolf S., Ylen P., Younsi A., Zaaroor M., Zhihui Y., Ziverte A., Zumbo F., Section Neuropsychology, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, RS: FPN NPPP I, Psychiatrie & Neuropsychologie, Menon, David [0000-0002-3228-9692], Ročka, Saulius, Molecular Neuroscience and Ageing Research (MOLAR), CENTER-TBI Investigators, van Veen, E, van der Jagt, M, Cnossen, M, Maas, A, de Beaufort, I, Menon, D, Citerio, G, Stocchetti, N, Rietdijk, W, van Dijck, J, and Kompanje, E
Subjects: Neurology, Internationality, Traumatic/complications, brain death, ethics, postmortem organ donation, traumatic brain injury, ventricular drainage, withdrawing life-sustaining measures, GUIDELINES, Critical Care and Intensive Care Medicine, 0302 clinical medicine, Traumatic brain injury, Trauma Centers, WORLDWIDE, Surveys and Questionnaires, Brain Injuries, Traumatic, Response rate (survey), Brain death, VDP::Medical disciplines: 700::Clinical medical disciplines: 750, VDP::Medisinske Fag: 700::Klinisk medisinske fag: 750, lcsh:Medical emergencies. Critical care. Intensive care. First aid, Tissue and Organ Procurement/legislation & jurisprudence, POLICIES, Europe, VARIABILITY, Neurosurgery, Clinical evaluation, medicine.medical_specialty, Tissue and Organ Procurement, Withdrawing life-sustaining measure, Other Research Radboud Institute for Molecular Life Sciences [Radboudumc 0], Healthcare improvement science Radboud Institute for Health Sciences [Radboudumc 18], 03 medical and health sciences, Neurological assessment, medicine, Humans, Organ donation, Ethics, Postmortem organ donation, Ventricular drainage, Withdrawing life-sustaining measures, Ethic, business.industry, Research, Brain Injuries, Traumatic/complications, 3112 Neurosciences, 030208 emergency & critical care medicine, ADULTS, lcsh:RC86-88.9, Traumatic brain injury, Brain death, Ethics, Postmortem organ donation, Withdrawing life-sustaining measures, Ventricular drainage, medicine.disease, 3126 Surgery, anesthesiology, intensive care, radiology, Reconstructive and regenerative medicine Radboud Institute for Health Sciences [Radboudumc 10], Trauma Centers/organization & administration, Brain Injuries, Emergency medicine, Human medicine, business, 030217 neurology & neurosurgery, Regional differences
Abstract: Source at https://doi.org/10.1186/s13054-018-2241-4. Licensed CC BY-NC-ND 4.0. Background: We aimed to investigate the extent of the agreement on practices around brain death and postmortem organ donation. Methods: Investigators from 67 Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study centers completed several questionnaires (response rate: 99%). Results: Regarding practices around brain death, we found agreement on the clinical evaluation (prerequisites and neurological assessment) for brain death determination (BDD) in 100% of the centers. However, ancillary tests were required for BDD in 64% of the centers. BDD for nondonor patients was deemed mandatory in 18% of the centers before withdrawing life-sustaining measures (LSM). Also, practices around postmortem organ donation varied. Organ donation after circulatory arrest was forbidden in 45% of the centers. When withdrawal of LSM was contemplated, in 67% of centers the patients with a ventricular drain in situ had this removed, either sometimes or all of the time. Conclusions: This study showed both agreement and some regional differences regarding practices around brain death and postmortem organ donation. We hope our results help quantify and understand potential differences, and provide impetus for current dialogs toward further harmonization of practices around brain death and postmortem organ donation.
Published: 2018

14. Variation in neurosurgical management of traumatic brain injury: a survey in 68 centers participating in the CENTER-TBI study (vol 161, pg 453, 2019)

Author: den Boogert H, Cnossen M, Lingsma H, Peul W, Ackerlund C, Adams H, Agnoletti V, Allanson J, Amrein K, Andaluz N, Andelic N, Andreassen L, Antun A, Anke A, Antoni A, Ardon H, Audibert G, Auslands K, Azouvi P, Azzolini M, Baciu C, Badenes R, Bartels R, Barzo P, Bauerfeind U, Beauvais R, Beer R, Belda F, Bellander B, Belli A, Bellier R, Benali H, Benard T, Berardino M, Beretta L, Beynon C, Bilotta F, Binder H, Biqiri E, Blaabjerg M, Bouzat P, Bragge P, Brazinova A, Brinck V, Brooker J, Brorsson C, Buki A, Bullinger M, Calappi E, Calvi M, Cameron P, Carbayo L, Carbonara M, Carise E, Carpenter K, Castano-Leon A, Causin F, Chevallard G, Chieregato A, Citerio G, Coburn M, Coles J, Coles-Kemp L, Collett J, Cooper J, Correia M, Covic A, Curry N, Czeiter E, Czosnyka M, Dahyot-Fizelier C, Damas F, Damas P, Dawes H, De Keyser V, Francesco D, Depreitere B, de Ruiter G, Dilvesi D, Ding S, Dippel D, Dixit A, Donoghue E, Dreier J, Duliere G, Eapen G, Engemann H, Ercole A, Esser P, Ezer E, Fabricius M, Feigin V, Feng J, Foks K, Fossi F, Francony G, Freo U, Frisvold S, Furmanov A, Gagliardo P, Galanaud D, Gantner D, Gao G, Geleijns K, George P, Ghuysen A, Giga L, Giraud B, Ben Glocker, Golubovic J, Gomez P, Grossi F, Gruen R, Gupta D, Haagsma J, Haitsma I, Hartings J, Helbok R, Helseth E, Hertle D, Hoedemaekers A, Hoefer S, Horton L, Huijben J, Hutchinson P, Haberg A, Jacobs B, Jankowski S, Jarrett M, Jelaca B, Jiang J, Jones K, Kamnitsas K, Karan M, Katila A, Kaukonen M, Kerforne T, Kivisaari R, Kolias A, Kolumban B, Kompanje E, Kolundzija K, Kondziella D, Koskinen L, Kovacs N, Lagares A, Lanyon L, Laureys S, Lecky F, Ledig C, Lefering R, Legrand V, Lei J, Levi L, Lightfoot R, Loeckx D, Lozano A, Maas A, MacDonald S, Maegele M, Majdan M, Major S, Manara A, Manley G, Martin D, Martin L, Martino C, Maruenda A, Marechal H, Masala A, Mattern J, McFadyen C, McMahon C, Melegh B, Menon D, Menovsky T, Morganti-Kossmann C, Mulazzi D, Muraleedharan V, Murray L, Muhlan H, Nair N, Negru A, Nelson D, Newcombe V, Nieboer D, Noirhomme Q, Nyiradi J, Oddo M, Oldenbeuving A, Oresic M, Ortolano F, Palotie A, Parizel P, Patruno A, Payen J, Perera N, Perlbarg V, Persona P, Piippo-Karjalainen A, Pili F, Pirinen M, Ples H, Poca M, Polinder S, Pomposo I, Posti J, Puybasset L, Radoi A, Ragauskas A, Raj R, Rambadagalla M, Real R, Rehorcikova V, Rhodes J, Ripatti S, Rocka S, Roe C, Roise O, Roks G, Rosand J, Rosenfeld J, Rosenlund C, Rosenthal G, Rossaint R, Rossi S, Rueckert D, Rusnak M, Sacchi M, Sahakian B, Sahuquillo J, Sakowitz O, Sala F, Sanchez-Porras R, Sandor J, Santos E, Sasu L, Savo D, Schaffer N, Schipper I, Schlosser B, Schmidt S, Schoechl H, Schoonman G, Schou R, Schwendenwein E, Scholl M, Sir O, Skandsen T, Smakman L, Smeets D, Smielewski P, Sorinola A, Stamatakis E, Stanworth S, Steinbuchel N, Stevanovic A, Stevens R, Stewart W, Steyerberg E, Stocchetti N, Sundstrom N, Synnot A, Taccone F, Takala R, Tamas V, Tanskanen P, Taylor M, Ao B, Tenovuo O, Telgmann R, Teodorani G, Theadom A, Thomas M, Tibboel D, Tolias C, Tshibanda J, Trapani T, Tudora C, Vajkoczy P, Vallance S, Valeinis E, Van der Steen G, van der Jagt M, van der Naalt J, van Dijck J, van Essen T, van Hecke W, van Heugten C, van Praag D, Vyvere T, Van Waesberghe J, Vanhaudenhuyse A, Vargiolu A, Vega E, Velt K, Verheyden J, Vespa P, Vik A, Vilcinis R, Vizzino G, Vleggeert-Lankamp C, Volovici V, Voormolen D, Vulekovic P, Vamos Z, Wade D, Wang K, Wang L, Wessels L, Wildschut E, Williams G, Wilson L, Winkler M, Wolf S, Ylen P, Younsi A, Zaaroor M, Yang Z, Ziverte A, Zumbo F, and CENTER-TBI Investigators
Published: 2019

15. Domain Generalization via Model-Agnostic Learning of Semantic Features

Author: Dou, Q, Coelho De Castro, D, Kamnitsas, K, Glocker, B, Engineering & Physical Science Research Council (E, and Commission of the European Communities
Subjects: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, cs.CV
Abstract: Generalization capability to unseen domains is crucial for machine learning models when deploying to real-world conditions. We investigate the challenging problem of domain generalization, i.e., training a model on multi-domain source data such that it can directly generalize to target domains with unknown statistics. We adopt a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift. Further, we introduce two complementary losses which explicitly regularize the semantic structure of the feature space. Globally, we align a derived soft confusion matrix to preserve general knowledge about inter-class relationships. Locally, we promote domain-independent class-specific cohesion and separation of sample features with a metric-learning component. The effectiveness of our method is demonstrated with new state-of-the-art results on two common object recognition benchmarks. Our method also shows consistent improvement on a medical image segmentation task., Comment: NeurIPS 2019
Published: 2019
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16. Variation in structure and process of care in traumatic brain injury: Provider profiles of European Neurotrauma Centers participating in the CENTER-TBI study

Author: Cnossen, M, Polinder, S, Lingsma, H, Maas, A, Menon, D, Steyerberg, E, Adams, H, Alessandro, M, Allanson, J, Amrein, K, Andaluz, N, Andelic, N, Andrea, N, Andreassen, L, Anke, A, Antoni, A, Ardon, H, Audibert, G, Auslands, K, Azouvi, P, Baciu, C, Bacon, A, Badenes, R, Baglin, T, Bartels, R, Barzo, P, Bauerfeind, U, Beer, R, Belda, F, Bellander, B, Belli, A, Bellier, R, Benali, H, Benard, T, Berardino, M, Beretta, L, Beynon, C, Bilotta, F, Binder, H, Biqiri, E, Blaabjerg, M, Borgen, L, Bouzat, P, Bragge, P, Brazinova, A, Brehar, F, Brorsson, C, Buki, A, Bullinger, M, Buckova, V, Calappi, E, Cameron, P, Carbayo, L, Carise, E, Carpenter, C, Castano-Leon, A, Causin, F, Chevallard, G, Chieregato, A, Citerio, G, Coburn, M, Coles, J, Cooper, J, Correia, M, Covic, A, Curry, N, Czeiter, E, Czosnyka, M, Dahyot-Fizelier, C, Damas, F, Damas, P, Dawes, H, De Keyser, V, Della Corte, F, Depreitere, B, Ding, S, Dippel, D, Dizdarevic, K, Duliere, G, Dzeko, A, Eapen, G, Engemann, H, Ercole, A, Esser, P, Ezer, E, Fabricius, M, Feigin, V, Feng, J, Foks, K, Fossi, F, Francony, G, Frantzen, J, Freo, U, Frisvold, S, Furmanov, A, Gagliardo, P, Galanaud, D, Gao, G, Geleijns, K, Ghuysen, A, Giraud, B, Glocker, B, Gomez, P, Grossi, F, Gruen, R, Gupta, D, Haagsma, J, Hadzic, E, Haitsma, I, Hartings, J, Helbok, R, Helseth, E, Hertle, D, Hill, S, Hoedemaekers, A, Hoefer, S, Hutchinson, P, Haberg, A, Jacobs, B, Janciak, I, Janssens, K, Jiang, J, Jones, K, Kalala, J, Kamnitsas, K, Karan, M, Karau, J, Katila, A, Kaukonen, M, Keeling, D, Kerforne, T, Ketharanathan, N, Kettunen, J, Kivisaari, R, Kolias, A, Kolumban, B, Kompanje, E, Kondziella, D, Koskinen, L, Kovacs, N, Kalovits, F, Lagares, A, Lanyon, L, Laureys, S, Lauritzen, M, Lecky, F, Ledig, C, Lefering, R, Legrand, V, Lei, J, Levi, L, Lightfoot, R, Loeckx, D, Lozano, A, Luddington, R, Luijten-Arts, C, Macdonald, S, Macfayden, C, Maegele, M, Majdan, M, Major, S, Manara, A, Manhes, P, Manley, G, Martin, D, Martino, C, Maruenda, A, Marechal, H, Mastelova, D, Mattern, J, Mcmahon, C, Melegh, B, Menovsky, T, Morganti-Kossmann, C, Mulazzi, D, Mutschler, M, Muhlan, H, Negru, A, Nelson, D, Neugebauer, E, Newcombe, V, Noirhomme, Q, Nyiradi, J, Oddo, M, Oldenbeuving, A, Oresic, M, Ortolano, F, Palotie, A, Parizel, P, Patruno, A, Payen, J, Perera, N, Perlbarg, V, Persona, P, Peul, W, Pichon, N, Piilgaard, H, Piippo, A, Pili, F, Pirinen, M, Ples, H, Pomposo, I, Psota, M, Pullens, P, Puybasset, L, Ragauskas, A, Raj, R, Rambadagalla, M, Rehorcikova, V, Rhodes, J, Richardson, S, Ripatti, S, Rocka, S, Rodier, N, Roe, C, Roise, O, Roks, G, Romegoux, P, Rosand, J, Rosenfeld, J, Rosenlund, C, Rosenthal, G, Rossaint, R, Rossi, S, Rostalski, T, Rueckert, D, Ruiz De Arcaute, F, Rusnak, M, Sacchi, M, Sahakian, B, Sahuquillo, J, Sakowitz, O, Sala, F, Sanchez-Pena, P, Sanchez-Porras, R, Sandor, J, Santos, E, Sasse, N, Sasu, L, Savo, D, Schipper, I, Schlosser, B, Schmidt, S, Schneider, A, Schoechl, H, Schoonman, G, Schou, R, Schwendenwein, E, Scholl, M, Sir, O, Skandsen, T, Smakman, L, Smeets, D, Smielewski, P, Sorinola, A, Stamatakis, E, Stanworth, S, Stegemann, K, Steinbuchel, N, Stevens, R, Stewart, W, Stocchetti, N, Sundstrom, N, Synnot, A, Szabo, J, Soderberg, J, Taccone, F, Tamas, V, Tanskanen, P, Tascu, A, Taylor, M, Te Ao, B, Tenovuo, O, Teodorani, G, Theadom, A, Thomas, M, Tibboel, D, Tolias, C, Tshibanda, J, Tudora, C, Vajkoczy, P, Valeinis, E, Van Hecke, W, Van Praag, D, Van Roost, D, Van Vlierberghe, E, Vande Vyvere, T, Vanhaudenhuyse, A, Vargiolu, A, Vega, E, Verheyden, J, Vespa, P, Vik, A, Vilcinis, R, Vizzino, G, Vleggeert-Lankamp, C, Volovici, V, Vulekovic, P, Vamos, Z, Wade, D, Wang, K, Wang, L, Wildschut, E, Williams, G, Willumsen, L, Wilson, A, Wilson, L, Winkler, M, Ylen, P, Younsi, A, Zaaroor, M, Zhang, Z, Zheng, Z, Zumbo, F, De Lange, S, De Ruiter, G, Den Boogert, H, Van Dijck, J, Van Essen, T, Van Heugten, C, Van Der Jagt, M, Van Der Naalt, J, Cnossen M. C., Polinder S., Lingsma H. F., Maas A. I. R., Menon D., Steyerberg E. W., Adams H., Alessandro M., Allanson J., Amrein K., Andaluz N., Andelic N., Andrea N., Andreassen L., Anke A., Antoni A., Ardon H., Audibert G., Auslands K., Azouvi P., Baciu C., Bacon A., Badenes R., Baglin T., Bartels R., Barzo P., Bauerfeind U., Beer R., Belda F. J., Bellander B. -M., Belli A., Bellier R., Benali H., Benard T., Berardino M., Beretta L., Beynon C., Bilotta F., Binder H., Biqiri E., Blaabjerg M., Borgen L. S., Bouzat P., Bragge P., Brazinova A., Brehar F., Brorsson C., Buki A., Bullinger M., Buckova V., Calappi E., Cameron P., Carbayo L. G., Carise E., Carpenter C., Castano-Leon A. M., Causin F., Chevallard G., Chieregato A., Citerio G., Coburn M. C., Coles J., Cooper J. D., Correia M., Covic A., Curry N., Czeiter E., Czosnyka M., Dahyot-Fizelier C., Damas F., Damas P., Dawes H., De Keyser V., Della Corte F., Depreitere B., Ding S., Dippel D., Dizdarevic K., Duliere G. -L., Dzeko A., Eapen G., Engemann H., Ercole A., Esser P., Ezer E., Fabricius M., Feigin V. L., Feng J., Foks K., Fossi F., Francony G., Frantzen J., Freo U., Frisvold S., Furmanov A., Gagliardo P., Galanaud D., Gao G., Geleijns K., Ghuysen A., Giraud B., Glocker B., Gomez P. A., Grossi F., Gruen R. L., Gupta D., Haagsma J. A., Hadzic E., Haitsma I., Hartings J. A., Helbok R., Helseth E., Hertle D., Hill S., Hoedemaekers A., Hoefer S., Hutchinson P. J., Haberg A. K., Jacobs B., Janciak I., Janssens K., Jiang J., Jones K., Kalala J. -P., Kamnitsas K., Karan M., Karau J., Katila A., Kaukonen M., Keeling D., Kerforne T., Ketharanathan N., Kettunen J., Kivisaari R., Kolias A. G., Kolumban B., Kompanje E., Kondziella D., Koskinen L. -O., Kovacs N., Kalovits F., Lagares A., Lanyon L., Laureys S., Lauritzen M., Lecky F., Ledig C., Lefering R., Legrand V., Lei J., Levi L., Lightfoot R., Lingsma H., Loeckx D., Lozano A., Luddington R., Luijten-Arts C., MacDonald S., MacFayden C., Maegele M., Majdan M., Major S., Manara A., Manhes P., Manley G., Martin D., Martino C., Maruenda A., Marechal H., Mastelova D., Mattern J., McMahon C., Melegh B., Menovsky T., Morganti-Kossmann C., Mulazzi D., Mutschler M., Muhlan H., Negru A., Nelson D., Neugebauer E., Newcombe V., Noirhomme Q., Nyiradi J., Oddo M., Oldenbeuving A., Oresic M., Ortolano F., Palotie A., Parizel P. M., Patruno A., Payen J. -F., Perera N., Perlbarg V., Persona P., Peul W., Pichon N., Piilgaard H., Piippo A., Pili F. S., Pirinen M., Ples H., Pomposo I., Psota M., Pullens P., Puybasset L., Ragauskas A., Raj R., Rambadagalla M., Rehorcikova V., Rhodes J., Richardson S., Ripatti S., Rocka S., Rodier N., Roe C., Roise O., Roks G., Romegoux P., Rosand J., Rosenfeld J., Rosenlund C., Rosenthal G., Rossaint R., Rossi S., Rostalski T., Rueckert D. L., Ruiz De Arcaute F., Rusnak M., Sacchi M., Sahakian B., Sahuquillo J., Sakowitz O., Sala F., Sanchez-Pena P., Sanchez-Porras R., Sandor J., Santos E., Sasse N., Sasu L., Savo D., Schipper I., Schlosser B., Schmidt S., Schneider A., Schoechl H., Schoonman G., Schou R. F., Schwendenwein E., Scholl M., Sir O., Skandsen T., Smakman L., Smeets D., Smielewski P., Sorinola A., Stamatakis E. L., Stanworth S., Stegemann K., Steinbuchel N., Stevens R., Stewart W., Stocchetti N., Sundstrom N., Synnot A., Szabo J., Soderberg J., Taccone F. S., Tamas V., Tanskanen P., Tascu A., Taylor M. S., Te Ao B., Tenovuo O., Teodorani G., Theadom A., Thomas M., Tibboel D., Tolias C., Tshibanda J. -F. L., Tudora C. M., Vajkoczy P., Valeinis E., Van Hecke W., Van Praag D., Van Roost D., Van Vlierberghe E., Vande Vyvere T., Vanhaudenhuyse A., Vargiolu A., Vega E., Verheyden J., Vespa P. M., Vik A., Vilcinis R., Vizzino G., Vleggeert-Lankamp C., Volovici V., Vulekovic P., Vamos Z., Wade D., Wang K. K. W., Wang L., Wildschut E., Williams G., Willumsen L., Wilson A., Wilson L., Winkler M. K. L., Ylen P., Younsi A., Zaaroor M., Zhang Z., Zheng Z., Zumbo F., De Lange S., De Ruiter G. C. W., Den Boogert H., Van Dijck J., Van Essen T. A., Van Heugten C., Van Der Jagt M., Van Der Naalt J., Cnossen, M, Polinder, S, Lingsma, H, Maas, A, Menon, D, Steyerberg, E, Adams, H, Alessandro, M, Allanson, J, Amrein, K, Andaluz, N, Andelic, N, Andrea, N, Andreassen, L, Anke, A, Antoni, A, Ardon, H, Audibert, G, Auslands, K, Azouvi, P, Baciu, C, Bacon, A, Badenes, R, Baglin, T, Bartels, R, Barzo, P, Bauerfeind, U, Beer, R, Belda, F, Bellander, B, Belli, A, Bellier, R, Benali, H, Benard, T, Berardino, M, Beretta, L, Beynon, C, Bilotta, F, Binder, H, Biqiri, E, Blaabjerg, M, Borgen, L, Bouzat, P, Bragge, P, Brazinova, A, Brehar, F, Brorsson, C, Buki, A, Bullinger, M, Buckova, V, Calappi, E, Cameron, P, Carbayo, L, Carise, E, Carpenter, C, Castano-Leon, A, Causin, F, Chevallard, G, Chieregato, A, Citerio, G, Coburn, M, Coles, J, Cooper, J, Correia, M, Covic, A, Curry, N, Czeiter, E, Czosnyka, M, Dahyot-Fizelier, C, Damas, F, Damas, P, Dawes, H, De Keyser, V, Della Corte, F, Depreitere, B, Ding, S, Dippel, D, Dizdarevic, K, Duliere, G, Dzeko, A, Eapen, G, Engemann, H, Ercole, A, Esser, P, Ezer, E, Fabricius, M, Feigin, V, Feng, J, Foks, K, Fossi, F, Francony, G, Frantzen, J, Freo, U, Frisvold, S, Furmanov, A, Gagliardo, P, Galanaud, D, Gao, G, Geleijns, K, Ghuysen, A, Giraud, B, Glocker, B, Gomez, P, Grossi, F, Gruen, R, Gupta, D, Haagsma, J, Hadzic, E, Haitsma, I, Hartings, J, Helbok, R, Helseth, E, Hertle, D, Hill, S, Hoedemaekers, A, Hoefer, S, Hutchinson, P, Haberg, A, Jacobs, B, Janciak, I, Janssens, K, Jiang, J, Jones, K, Kalala, J, Kamnitsas, K, Karan, M, Karau, J, Katila, A, Kaukonen, M, Keeling, D, Kerforne, T, Ketharanathan, N, Kettunen, J, Kivisaari, R, Kolias, A, Kolumban, B, Kompanje, E, Kondziella, D, Koskinen, L, Kovacs, N, Kalovits, F, Lagares, A, Lanyon, L, Laureys, S, Lauritzen, M, Lecky, F, Ledig, C, Lefering, R, Legrand, V, Lei, J, Levi, L, Lightfoot, R, Loeckx, D, Lozano, A, Luddington, R, Luijten-Arts, C, Macdonald, S, Macfayden, C, Maegele, M, Majdan, M, Major, S, Manara, A, Manhes, P, Manley, G, Martin, D, Martino, C, Maruenda, A, Marechal, H, Mastelova, D, Mattern, J, Mcmahon, C, Melegh, B, Menovsky, T, Morganti-Kossmann, C, Mulazzi, D, Mutschler, M, Muhlan, H, Negru, A, Nelson, D, Neugebauer, E, Newcombe, V, Noirhomme, Q, Nyiradi, J, Oddo, M, Oldenbeuving, A, Oresic, M, Ortolano, F, Palotie, A, Parizel, P, Patruno, A, Payen, J, Perera, N, Perlbarg, V, Persona, P, Peul, W, Pichon, N, Piilgaard, H, Piippo, A, Pili, F, Pirinen, M, Ples, H, Pomposo, I, Psota, M, Pullens, P, Puybasset, L, Ragauskas, A, Raj, R, Rambadagalla, M, Rehorcikova, V, Rhodes, J, Richardson, S, Ripatti, S, Rocka, S, Rodier, N, Roe, C, Roise, O, Roks, G, Romegoux, P, Rosand, J, Rosenfeld, J, Rosenlund, C, Rosenthal, G, Rossaint, R, Rossi, S, Rostalski, T, Rueckert, D, Ruiz De Arcaute, F, Rusnak, M, Sacchi, M, Sahakian, B, Sahuquillo, J, Sakowitz, O, Sala, F, Sanchez-Pena, P, Sanchez-Porras, R, Sandor, J, Santos, E, Sasse, N, Sasu, L, Savo, D, Schipper, I, Schlosser, B, Schmidt, S, Schneider, A, Schoechl, H, Schoonman, G, Schou, R, Schwendenwein, E, Scholl, M, Sir, O, Skandsen, T, Smakman, L, Smeets, D, Smielewski, P, Sorinola, A, Stamatakis, E, Stanworth, S, Stegemann, K, Steinbuchel, N, Stevens, R, Stewart, W, Stocchetti, N, Sundstrom, N, Synnot, A, Szabo, J, Soderberg, J, Taccone, F, Tamas, V, Tanskanen, P, Tascu, A, Taylor, M, Te Ao, B, Tenovuo, O, Teodorani, G, Theadom, A, Thomas, M, Tibboel, D, Tolias, C, Tshibanda, J, Tudora, C, Vajkoczy, P, Valeinis, E, Van Hecke, W, Van Praag, D, Van Roost, D, Van Vlierberghe, E, Vande Vyvere, T, Vanhaudenhuyse, A, Vargiolu, A, Vega, E, Verheyden, J, Vespa, P, Vik, A, Vilcinis, R, Vizzino, G, Vleggeert-Lankamp, C, Volovici, V, Vulekovic, P, Vamos, Z, Wade, D, Wang, K, Wang, L, Wildschut, E, Williams, G, Willumsen, L, Wilson, A, Wilson, L, Winkler, M, Ylen, P, Younsi, A, Zaaroor, M, Zhang, Z, Zheng, Z, Zumbo, F, De Lange, S, De Ruiter, G, Den Boogert, H, Van Dijck, J, Van Essen, T, Van Heugten, C, Van Der Jagt, M, Van Der Naalt, J, Cnossen M. C., Polinder S., Lingsma H. F., Maas A. I. R., Menon D., Steyerberg E. W., Adams H., Alessandro M., Allanson J., Amrein K., Andaluz N., Andelic N., Andrea N., Andreassen L., Anke A., Antoni A., Ardon H., Audibert G., Auslands K., Azouvi P., Baciu C., Bacon A., Badenes R., Baglin T., Bartels R., Barzo P., Bauerfeind U., Beer R., Belda F. J., Bellander B. -M., Belli A., Bellier R., Benali H., Benard T., Berardino M., Beretta L., Beynon C., Bilotta F., Binder H., Biqiri E., Blaabjerg M., Borgen L. S., Bouzat P., Bragge P., Brazinova A., Brehar F., Brorsson C., Buki A., Bullinger M., Buckova V., Calappi E., Cameron P., Carbayo L. G., Carise E., Carpenter C., Castano-Leon A. M., Causin F., Chevallard G., Chieregato A., Citerio G., Coburn M. C., Coles J., Cooper J. D., Correia M., Covic A., Curry N., Czeiter E., Czosnyka M., Dahyot-Fizelier C., Damas F., Damas P., Dawes H., De Keyser V., Della Corte F., Depreitere B., Ding S., Dippel D., Dizdarevic K., Duliere G. -L., Dzeko A., Eapen G., Engemann H., Ercole A., Esser P., Ezer E., Fabricius M., Feigin V. L., Feng J., Foks K., Fossi F., Francony G., Frantzen J., Freo U., Frisvold S., Furmanov A., Gagliardo P., Galanaud D., Gao G., Geleijns K., Ghuysen A., Giraud B., Glocker B., Gomez P. A., Grossi F., Gruen R. L., Gupta D., Haagsma J. A., Hadzic E., Haitsma I., Hartings J. A., Helbok R., Helseth E., Hertle D., Hill S., Hoedemaekers A., Hoefer S., Hutchinson P. J., Haberg A. K., Jacobs B., Janciak I., Janssens K., Jiang J., Jones K., Kalala J. -P., Kamnitsas K., Karan M., Karau J., Katila A., Kaukonen M., Keeling D., Kerforne T., Ketharanathan N., Kettunen J., Kivisaari R., Kolias A. G., Kolumban B., Kompanje E., Kondziella D., Koskinen L. -O., Kovacs N., Kalovits F., Lagares A., Lanyon L., Laureys S., Lauritzen M., Lecky F., Ledig C., Lefering R., Legrand V., Lei J., Levi L., Lightfoot R., Lingsma H., Loeckx D., Lozano A., Luddington R., Luijten-Arts C., MacDonald S., MacFayden C., Maegele M., Majdan M., Major S., Manara A., Manhes P., Manley G., Martin D., Martino C., Maruenda A., Marechal H., Mastelova D., Mattern J., McMahon C., Melegh B., Menovsky T., Morganti-Kossmann C., Mulazzi D., Mutschler M., Muhlan H., Negru A., Nelson D., Neugebauer E., Newcombe V., Noirhomme Q., Nyiradi J., Oddo M., Oldenbeuving A., Oresic M., Ortolano F., Palotie A., Parizel P. M., Patruno A., Payen J. -F., Perera N., Perlbarg V., Persona P., Peul W., Pichon N., Piilgaard H., Piippo A., Pili F. S., Pirinen M., Ples H., Pomposo I., Psota M., Pullens P., Puybasset L., Ragauskas A., Raj R., Rambadagalla M., Rehorcikova V., Rhodes J., Richardson S., Ripatti S., Rocka S., Rodier N., Roe C., Roise O., Roks G., Romegoux P., Rosand J., Rosenfeld J., Rosenlund C., Rosenthal G., Rossaint R., Rossi S., Rostalski T., Rueckert D. L., Ruiz De Arcaute F., Rusnak M., Sacchi M., Sahakian B., Sahuquillo J., Sakowitz O., Sala F., Sanchez-Pena P., Sanchez-Porras R., Sandor J., Santos E., Sasse N., Sasu L., Savo D., Schipper I., Schlosser B., Schmidt S., Schneider A., Schoechl H., Schoonman G., Schou R. F., Schwendenwein E., Scholl M., Sir O., Skandsen T., Smakman L., Smeets D., Smielewski P., Sorinola A., Stamatakis E. L., Stanworth S., Stegemann K., Steinbuchel N., Stevens R., Stewart W., Stocchetti N., Sundstrom N., Synnot A., Szabo J., Soderberg J., Taccone F. S., Tamas V., Tanskanen P., Tascu A., Taylor M. S., Te Ao B., Tenovuo O., Teodorani G., Theadom A., Thomas M., Tibboel D., Tolias C., Tshibanda J. -F. L., Tudora C. M., Vajkoczy P., Valeinis E., Van Hecke W., Van Praag D., Van Roost D., Van Vlierberghe E., Vande Vyvere T., Vanhaudenhuyse A., Vargiolu A., Vega E., Verheyden J., Vespa P. M., Vik A., Vilcinis R., Vizzino G., Vleggeert-Lankamp C., Volovici V., Vulekovic P., Vamos Z., Wade D., Wang K. K. W., Wang L., Wildschut E., Williams G., Willumsen L., Wilson A., Wilson L., Winkler M. K. L., Ylen P., Younsi A., Zaaroor M., Zhang Z., Zheng Z., Zumbo F., De Lange S., De Ruiter G. C. W., Den Boogert H., Van Dijck J., Van Essen T. A., Van Heugten C., Van Der Jagt M., and Van Der Naalt J.
Abstract: Introduction: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Methods: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. Results: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. Conclusion: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches.
Published: 2016

17. Semi-Supervised Learning via Compact Latent Space Clustering

Author: Kamnitsas, K., Castro, D. C., Loïc Le Folgoc, Walker, I., Tanno, R., Rueckert, D., Glocker, B., Criminisi, A., Nori, A., Engineering & Physical Science Research Council (E, and Commission of the European Communities
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), cs.LG, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing, Machine Learning (stat.ML), stat.ML, Machine Learning (cs.LG), ComputingMethodologies_PATTERNRECOGNITION, Statistics - Machine Learning, Neural and Evolutionary Computing (cs.NE), cs.NE, cs.CV
Abstract: We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to dynamically create a graph over embeddings of labeled and unlabeled samples of a training batch to capture underlying structure in feature space, and use label propagation to estimate its high and low density regions. We then devise a cost function based on Markov chains on the graph that regularizes the latent space to form a single compact cluster per class, while avoiding to disturb existing clusters during optimization. We evaluate our approach on three benchmarks and compare to state-of-the art with promising results. Our approach combines the benefits of graph-based regularization with efficient, inductive inference, does not require modifications to a network architecture, and can thus be easily applied to existing networks to enable an effective use of unlabeled data., Presented as a long oral in ICML 2018. Post-conference camera ready
Published: 2018

18. Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI

Author: Winzeck, S., primary, Mocking, S.J.T., additional, Bezerra, R., additional, Bouts, M.J.R.J., additional, McIntosh, E.C., additional, Diwan, I., additional, Garg, P., additional, Chutinet, A., additional, Kimberly, W.T., additional, Copen, W.A., additional, Schaefer, P.W., additional, Ay, H., additional, Singhal, A.B., additional, Kamnitsas, K., additional, Glocker, B., additional, Sorensen, A.G., additional, and Wu, O., additional
Published: 2019
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19. Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

Author: Oktay, O, Ferrante, E, Kamnitsas, K, Heinrich, M, Bai, W, Caballero, J, Cook, S, De Marvao, A, Dawes, T, O'Regan, D, Kainz, B, Glocker, B, Rueckert, D, Engineering & Physical Science Research Council (EPSRC), British Heart Foundation, and Imperial College Healthcare NHS Trust- BRC Funding
Subjects: FOS: Computer and information sciences, Technology, Databases, Factual, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, convolutional neural network, 09 Engineering, Engineering, Imaging, Three-Dimensional, Humans, AUTOMATIC SEGMENTATION, Imaging Science & Photographic Technology, Engineering, Biomedical, image super-resolution, cs.CV, medical image segmentation, Science & Technology, VENTRICLE, Radiology, Nuclear Medicine & Medical Imaging, Engineering, Electrical & Electronic, Heart, Magnetic Resonance Imaging, SHAPE MODELS, Cardiac Imaging Techniques, Nuclear Medicine & Medical Imaging, Computer Science, Shape prior, Computer Science, Interdisciplinary Applications, Neural Networks, Computer, 08 Information and Computing Sciences, Cardiomyopathies, Life Sciences & Biomedicine, Algorithms
Abstract: Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learned non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac datasets and public benchmarks. Additionally, we demonstrate how the learned deep models of 3D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies., Comment: Published in IEEE Transactions on Medical Imaging (Aug 2017)
Published: 2017
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20. Variation in monitoring and treatment policies for intracranial hypertension in traumatic brain injury: A survey in 66 neurotrauma centers participating in the CENTER-TBI study

Author: Cnossen, M.C. (Maryse), Huijben, J.A. (Jilske A.), van der Jagt, M. (Mathieu), Volovici, V. (Victor), van Essen, T. (Thomas), Polinder, S. (Suzanne), Nelson, D. (David), Ercole, A. (Ari), Stocchetti, N. (Nino), Citerio, G. (Giuseppe), Peul, W.C. (Wilco), Maas, A.I.R. (Andrew), Menon, D.K. (David ), Steyerberg, E.W. (Ewout), Lingsma, H.F. (Hester F.), Adams, H. (Hadie), Alessandro, M. (Masala), Allanson, J.E. (Judith), Amrein, K. (Krisztina), Andaluz, N. (Norberto), Andelic, N. (Nada), Andrea, N. (Nanni), Andreassen, L. (Lasse), Anke, A. (Audny), Antoni, A. (Anna), Ardon, H. (Hilko), Audibert, G. (Gérard), Auslands, K. (Kaspars), Azouvi, P. (Philippe), Baciu, C. (Camelia), Bacon, A. (Andrew), Badenes, R. (Rafael), Baglin, T. (Trevor), Bartels, R.H.M.A. (Ronald), Barzo, P. (P.), Bauerfeind, U. (Ursula), Beer, R. (Ronny), Belda, F.J. (Francisco Javier), Bellander, B.-M. (Bo-Michael), Belli, A. (Antonio), Bellier, R. (Rémy), Benali, H. (Habib), Benard, T. (Thierry), Berardino, M. (Maurizio), Beretta, L. (Luigi), Beynon, C. (Christopher), Bilotta, F. (Federico), Binder, H. (Harald), Biqiri, E. (Erta), Blaabjerg, M. (Morten), Lund, S.B. (Stine Borgen), Bouzat, P. (Pierre), Bragge, P. (Peter), Brazinova, A. (Alexandra), Brehar, F. (Felix), Brorsson, C. (Camilla), Buki, A. (Andras), Bullinger, M. (Monika), Bucková, V. (Veronika), Calappi, E. (Emiliana), Cameron, P. (Peter), Carbayo, L.G. (Lozano Guillermo), Carise, E. (Elsa), Carpenter, K.L.H. (Keri L.H.), Castaño-León, A.M. (Ana M.), Causin, F. (Francesco), Chevallard, G. (Giorgio), Chieregato, A. (Arturo), Cnossen, M. (Maryse), Coburn, M. (Mark), Coles, J.P. (Jonathan P.), Cooper, J.D. (Jamie D.), Correia, M. (Marta), Covic, A. (Amra), Curry, N. (Nicola), Czeiter, E. (Endre), Czosnyka, M. (Marek), Dahyot-Fizelier, C. (Claire), Damas, F. (François), Damas, P. (Pierre), Dawes, H. (Helen), De Keyser, V. (Véronique), Corte, F.D. (Francesco), Depreitere, B. (Bart), Ding, S. (Shenghao), Dippel, D.W.J. (Diederik), Dizdarevic, K. (Kemal), Dulière, G.-L. (Guy-Loup), Dzeko, A. (Adelaida), Eapen, G. (George), Engemann, H. (Heiko), Esser, P. (Patrick), Ezer, E. (Erzsébet), Fabricius, M. (Martin), Feigin, V.L. (V.), Feng, J. (Junfeng), Foks, K. (Kelly), Fossi, F. (Francesca), Francony, G. (Gilles), Frantzén, J. (Janek), Freo, U. (Ulderico), Frisvold, S.K. (Shirin Kordasti), Furmanov, A. (Alex), Gagliardo, P. (Pablo), Galanaud, D. (Damien), Gao, G. (Guoyi), Geleijns, K. (Karin), Ghuysen, A. (Alexandre), Giraud, B. (Benoit), Glocker, B. (Ben), Gomez, P.A. (Pedro A.), Grossi, F. (Francesca), Gruen, R.L. (Russell), Gupta, D. (Deepak), Haagsma, J.A. (Juanita), Hadzic, E. (Ermin), Haitsma, I. (Iain), Hartings, J.A. (Jed), Helbok, R. (Raimund), Helseth, E. (Eirik), Hertle, D. (Daniel), Hill, S. (Sean), Hoedemaekers, A. (Astrid), Hoefer, S. (Stefan), Hutchinson, P.J. (Peter J.), Håberg, K.A. (Kristine Asta), Jacobs, B.C. (Bart), Janciak, I. (Ivan), Janssens, K. (Koen), Jiang, J.-Y. (Ji-Yao), Jones, K. (Kelly), Kalala, J.-P. (Jean-Pierre), Kamnitsas, K. (Konstantinos), Karan, M. (Mladen), Karau, J. (Jana), Katila, A. (Ari), Kaukonen, M. (Maija), Keeling, D. (David), Kerforne, T. (Thomas), Ketharanathan, N. (Naomi), Kettunen, J. (Johannes), Kivisaari, R. (Riku), Kolias, A.G. (Angelos G.), Kolumbán, B. (Bálint), Kompanje, E.J.O. (Erwin), Kondziella, D. (Daniel), Koskinen, L.-O. (Lars-Owe), Kovács, N. (Noémi), Kalovits, F. (Ferenc), Lagares, A. (Alfonso), Lanyon, L. (Linda), Laureys, S. (Steven), Lauritzen, M. (Martin), Lecky, F.E. (Fiona), Ledig, C. (Christian), Lefering, R., Legrand, V. (Valerie), Lei, J. (Jin), Levi, L. (Leon), Lightfoot, R. (Roger), Lingsma, H.F. (Hester), Loeckx, D. (Dirk), Lozano, A. (Angels), Luddington, R. (Roger), Luijten-Arts, C. (Chantal), Maas, A.I.R. (Andrew I.R.), MacDonald, S. (Stephen), MacFayden, C. (Charles), Maegele, M. (Marc), Majdan, M. (Marek), Major, S. (Sebastian), Manara, A. (Alex), Manhes, P. (Pauline), Manley, G. (Geoffrey), Martin, D. (Didier), Martino, C. (Costanza), Maruenda, A. (Armando), Maréchal, H. (Hugues), Mastelova, D. (Dagmara), Mattern, J. (Julia), McMahon, C. (Catherine), Melegh, B. (Béla), Menon, D. (David), Menovsky, T. (Tomas), Morganti-Kossmann, C. (Cristina), Mulazzi, D. (Davide), Mutschler, M. (Manuel), Mühlan, H. (Holger), Negru, A. (Ancuta), Neugebauer, E. (Eddy), Newcombe, V.F. (Virginia F.), Noirhomme, Q. (Quentin), Nyirádi, J. (József), Oddo, M. (Mauro), Oldenbeuving, A.W., Oresic, M. (Matej), Ortolano, F. (Fabrizio), Palotie, A. (Aarno), Parizel, P.M., Patruno, A. (Adriana), Payen, J.-F. (Jean-François), Perera, N. (Natascha), Perlbarg, V. (Vincent), Persona, P. (Paolo), Peul, W. (Wilco), Pichon, N. (Nicolas), Piilgaard, H. (Henning), Piippo, A. (Anna), Floury, S.P. (Sébastien Pili), Pirinen, M. (Matti), Ples, H. (Horia), Pomposo, I. (Inigo), Psota, M. (Marek), Pullens, P. (Pim), Puybasset, L. (Louis), Ragauskas, A. (Arminas), Raj, R. (Rahul), Rambadagalla, M. (Malinka), Rehorcíková, V. (Veronika), Rhodes, J.K.J. (Jonathan K.J.), Richardson, S. (Sylvia), Ripatti, S. (Samuli), Rocka, S. (Saulius), Rodier, N. (Nicolas), Roe, C. (Cecilie), Roise, O. (Olav), Roks, C.M.A.A. (Gerwin), Romegoux, P. (Pauline), Rosand, J. (Jonathan), Rosenfeld, J. (Jeffrey), Rosenlund, C. (Christina), Rosenthal, G. (Guy), Rossaint, R. (Rolf), Rossi, S. (Sandra), Rostalski, T. (Tim), Rueckert, D. (Daniel), de Ruiz, A.F. (Arcaute Felix), Rusnák, M. (Martin), Sacchi, M. (Marco), Sahakian, B. (Barbara), Sahuquillo, J. (Juan), Sakowitz, O. (Oliver), Sala, F. (Francesca), Sanchez-Pena, P. (Paola), Sanchez-Porras, R. (Renan), Sandor, J. (Janos), Santos, E. (Edgar), Sasse, N. (Nadine), Sasu, L. (Luminita), Savo, D. (Davide), Schipper, I.B. (Inger), Schlößer, B. (Barbara), Schmidt, S. (Silke), Schneider, A. (Annette), Schoechl, H. (Herbert), Schoonman, G.G. (Guus), Rico, F.S. (Frederik Schou), Schwendenwein, E. (Elisabeth), Schöll, M. (Michael), Sir, O. (özcan), Skandsen, T. (Toril), Smakman, L. (Lidwien), Smeets, D. (Dominique), Smielewski, P. (Peter), Sorinola, A. (Abayomi), Stamatakis, E. (Emmanuel), Stanworth, S. (Simon), Stegemann, K. (Katrin), Steinbüchel, N. (Nicole), Stevens, R. (Robert), Stewart, W. (William), Sundström, N. (Nina), Synnot, A. (Anneliese), Szabó, J. (József), Söderberg, J. (Jeannette), Taccone, F.S. (Fabio), Tamás, V. (Viktória), Tanskanen, P. (Päivi), Tascu, A. (Alexandru), Taylor, M.S. (Mark Steven), Te, A.B. (Ao Braden), Tenovuo, O. (Olli), Teodorani, G. (Guido), Theadom, A. (Alice), Thomas, M. (Matt), Tibboel, D. (Dick), Tolias, C.M. (Christos M.), Tshibanda, J.-F.L. (Jean-Flory Luaba), Tudora, C.M. (Cristina Maria), Vajkoczy, P. (Peter), Valeinis, E. (Egils), Hecke, W.V. (Wim Van), Praag, D.V. (Dominique Van), Dirk, V.R. (Van Roost), Vlierberghe, E.V. (Eline Van), Vyvere, T.V. (Thijs vande), Vanhaudenhuyse, A. (Audrey), Vargiolu, A. (Alessia), Vega, E. (Emmanuel), Verheyden, J. (Jan), Vespa, P.M. (Paul M.), Vik, A. (Anne), Vilcinis, R. (Rimantas), Vizzino, G. (Giacinta), Vleggeert-Lankamp, C.L.A.M. (Carmen), Vulekovic, P. (Peter), Vámos, Z. (Zoltán), Wade, D. (Derick), Wang, K.K.W. (Kevin K.W.), Wang, L. (Lei), Wildschut, E.D. (Enno), Williams, G. (Guy), Willumsen, L. (Lisette), Wilson, A. (Adam), Wilson, L. (Lindsay), Winkler, M.K.L. (Maren K.L.), Ylén, P. (Peter), Younsi, A. (Alexander), Zaaroor, M. (Menashe), Zhang, Z. (Zhiqun), Zheng, Z. (Zelong), Zumbo, F. (Fabrizio), de Lange, S. (Stefanie), De Ruiter, G.C.W. (Godard C.W.), den Boogert, H. (Hugo), van Dijck, J. (Jeroen), Essen, T.A. (T.) van, van Heugten, C.M. (Caroline M.), Jagt, M. (Mathieu) van der, Naalt, J. (Joukje) van der, Cnossen, M.C. (Maryse), Huijben, J.A. (Jilske A.), van der Jagt, M. (Mathieu), Volovici, V. (Victor), van Essen, T. (Thomas), Polinder, S. (Suzanne), Nelson, D. (David), Ercole, A. (Ari), Stocchetti, N. (Nino), Citerio, G. (Giuseppe), Peul, W.C. (Wilco), Maas, A.I.R. (Andrew), Menon, D.K. (David ), Steyerberg, E.W. (Ewout), Lingsma, H.F. (Hester F.), Adams, H. (Hadie), Alessandro, M. (Masala), Allanson, J.E. (Judith), Amrein, K. (Krisztina), Andaluz, N. (Norberto), Andelic, N. (Nada), Andrea, N. (Nanni), Andreassen, L. (Lasse), Anke, A. (Audny), Antoni, A. (Anna), Ardon, H. (Hilko), Audibert, G. (Gérard), Auslands, K. (Kaspars), Azouvi, P. (Philippe), Baciu, C. (Camelia), Bacon, A. (Andrew), Badenes, R. (Rafael), Baglin, T. (Trevor), Bartels, R.H.M.A. (Ronald), Barzo, P. (P.), Bauerfeind, U. (Ursula), Beer, R. (Ronny), Belda, F.J. (Francisco Javier), Bellander, B.-M. (Bo-Michael), Belli, A. (Antonio), Bellier, R. (Rémy), Benali, H. (Habib), Benard, T. (Thierry), Berardino, M. (Maurizio), Beretta, L. (Luigi), Beynon, C. (Christopher), Bilotta, F. (Federico), Binder, H. (Harald), Biqiri, E. (Erta), Blaabjerg, M. (Morten), Lund, S.B. (Stine Borgen), Bouzat, P. (Pierre), Bragge, P. (Peter), Brazinova, A. (Alexandra), Brehar, F. (Felix), Brorsson, C. (Camilla), Buki, A. (Andras), Bullinger, M. (Monika), Bucková, V. (Veronika), Calappi, E. (Emiliana), Cameron, P. (Peter), Carbayo, L.G. (Lozano Guillermo), Carise, E. (Elsa), Carpenter, K.L.H. (Keri L.H.), Castaño-León, A.M. (Ana M.), Causin, F. (Francesco), Chevallard, G. (Giorgio), Chieregato, A. (Arturo), Cnossen, M. (Maryse), Coburn, M. (Mark), Coles, J.P. (Jonathan P.), Cooper, J.D. (Jamie D.), Correia, M. (Marta), Covic, A. (Amra), Curry, N. (Nicola), Czeiter, E. (Endre), Czosnyka, M. (Marek), Dahyot-Fizelier, C. (Claire), Damas, F. (François), Damas, P. (Pierre), Dawes, H. (Helen), De Keyser, V. (Véronique), Corte, F.D. (Francesco), Depreitere, B. (Bart), Ding, S. (Shenghao), Dippel, D.W.J. (Diederik), Dizdarevic, K. (Kemal), Dulière, G.-L. (Guy-Loup), Dzeko, A. (Adelaida), Eapen, G. (George), Engemann, H. (Heiko), Esser, P. (Patrick), Ezer, E. (Erzsébet), Fabricius, M. (Martin), Feigin, V.L. (V.), Feng, J. (Junfeng), Foks, K. (Kelly), Fossi, F. (Francesca), Francony, G. (Gilles), Frantzén, J. (Janek), Freo, U. (Ulderico), Frisvold, S.K. (Shirin Kordasti), Furmanov, A. (Alex), Gagliardo, P. (Pablo), Galanaud, D. (Damien), Gao, G. (Guoyi), Geleijns, K. (Karin), Ghuysen, A. (Alexandre), Giraud, B. (Benoit), Glocker, B. (Ben), Gomez, P.A. (Pedro A.), Grossi, F. (Francesca), Gruen, R.L. (Russell), Gupta, D. (Deepak), Haagsma, J.A. (Juanita), Hadzic, E. (Ermin), Haitsma, I. (Iain), Hartings, J.A. (Jed), Helbok, R. (Raimund), Helseth, E. (Eirik), Hertle, D. (Daniel), Hill, S. (Sean), Hoedemaekers, A. (Astrid), Hoefer, S. (Stefan), Hutchinson, P.J. (Peter J.), Håberg, K.A. (Kristine Asta), Jacobs, B.C. (Bart), Janciak, I. (Ivan), Janssens, K. (Koen), Jiang, J.-Y. (Ji-Yao), Jones, K. (Kelly), Kalala, J.-P. (Jean-Pierre), Kamnitsas, K. (Konstantinos), Karan, M. (Mladen), Karau, J. (Jana), Katila, A. (Ari), Kaukonen, M. (Maija), Keeling, D. (David), Kerforne, T. (Thomas), Ketharanathan, N. (Naomi), Kettunen, J. (Johannes), Kivisaari, R. (Riku), Kolias, A.G. (Angelos G.), Kolumbán, B. (Bálint), Kompanje, E.J.O. (Erwin), Kondziella, D. (Daniel), Koskinen, L.-O. (Lars-Owe), Kovács, N. (Noémi), Kalovits, F. (Ferenc), Lagares, A. (Alfonso), Lanyon, L. (Linda), Laureys, S. (Steven), Lauritzen, M. (Martin), Lecky, F.E. (Fiona), Ledig, C. (Christian), Lefering, R., Legrand, V. (Valerie), Lei, J. (Jin), Levi, L. (Leon), Lightfoot, R. (Roger), Lingsma, H.F. (Hester), Loeckx, D. (Dirk), Lozano, A. (Angels), Luddington, R. (Roger), Luijten-Arts, C. (Chantal), Maas, A.I.R. (Andrew I.R.), MacDonald, S. (Stephen), MacFayden, C. (Charles), Maegele, M. (Marc), Majdan, M. (Marek), Major, S. (Sebastian), Manara, A. (Alex), Manhes, P. (Pauline), Manley, G. (Geoffrey), Martin, D. (Didier), Martino, C. (Costanza), Maruenda, A. (Armando), Maréchal, H. (Hugues), Mastelova, D. (Dagmara), Mattern, J. (Julia), McMahon, C. (Catherine), Melegh, B. (Béla), Menon, D. (David), Menovsky, T. (Tomas), Morganti-Kossmann, C. (Cristina), Mulazzi, D. (Davide), Mutschler, M. (Manuel), Mühlan, H. (Holger), Negru, A. (Ancuta), Neugebauer, E. (Eddy), Newcombe, V.F. (Virginia F.), Noirhomme, Q. (Quentin), Nyirádi, J. (József), Oddo, M. (Mauro), Oldenbeuving, A.W., Oresic, M. (Matej), Ortolano, F. (Fabrizio), Palotie, A. (Aarno), Parizel, P.M., Patruno, A. (Adriana), Payen, J.-F. (Jean-François), Perera, N. (Natascha), Perlbarg, V. (Vincent), Persona, P. (Paolo), Peul, W. (Wilco), Pichon, N. (Nicolas), Piilgaard, H. (Henning), Piippo, A. (Anna), Floury, S.P. (Sébastien Pili), Pirinen, M. (Matti), Ples, H. (Horia), Pomposo, I. (Inigo), Psota, M. (Marek), Pullens, P. (Pim), Puybasset, L. (Louis), Ragauskas, A. (Arminas), Raj, R. (Rahul), Rambadagalla, M. (Malinka), Rehorcíková, V. (Veronika), Rhodes, J.K.J. (Jonathan K.J.), Richardson, S. (Sylvia), Ripatti, S. (Samuli), Rocka, S. (Saulius), Rodier, N. (Nicolas), Roe, C. (Cecilie), Roise, O. (Olav), Roks, C.M.A.A. (Gerwin), Romegoux, P. (Pauline), Rosand, J. (Jonathan), Rosenfeld, J. (Jeffrey), Rosenlund, C. (Christina), Rosenthal, G. (Guy), Rossaint, R. (Rolf), Rossi, S. (Sandra), Rostalski, T. (Tim), Rueckert, D. (Daniel), de Ruiz, A.F. (Arcaute Felix), Rusnák, M. (Martin), Sacchi, M. (Marco), Sahakian, B. (Barbara), Sahuquillo, J. (Juan), Sakowitz, O. (Oliver), Sala, F. (Francesca), Sanchez-Pena, P. (Paola), Sanchez-Porras, R. (Renan), Sandor, J. (Janos), Santos, E. (Edgar), Sasse, N. (Nadine), Sasu, L. (Luminita), Savo, D. (Davide), Schipper, I.B. (Inger), Schlößer, B. (Barbara), Schmidt, S. (Silke), Schneider, A. (Annette), Schoechl, H. (Herbert), Schoonman, G.G. (Guus), Rico, F.S. (Frederik Schou), Schwendenwein, E. (Elisabeth), Schöll, M. (Michael), Sir, O. (özcan), Skandsen, T. (Toril), Smakman, L. (Lidwien), Smeets, D. (Dominique), Smielewski, P. (Peter), Sorinola, A. (Abayomi), Stamatakis, E. (Emmanuel), Stanworth, S. (Simon), Stegemann, K. (Katrin), Steinbüchel, N. (Nicole), Stevens, R. (Robert), Stewart, W. (William), Sundström, N. (Nina), Synnot, A. (Anneliese), Szabó, J. (József), Söderberg, J. (Jeannette), Taccone, F.S. (Fabio), Tamás, V. (Viktória), Tanskanen, P. (Päivi), Tascu, A. (Alexandru), Taylor, M.S. (Mark Steven), Te, A.B. (Ao Braden), Tenovuo, O. (Olli), Teodorani, G. (Guido), Theadom, A. (Alice), Thomas, M. (Matt), Tibboel, D. (Dick), Tolias, C.M. (Christos M.), Tshibanda, J.-F.L. (Jean-Flory Luaba), Tudora, C.M. (Cristina Maria), Vajkoczy, P. (Peter), Valeinis, E. (Egils), Hecke, W.V. (Wim Van), Praag, D.V. (Dominique Van), Dirk, V.R. (Van Roost), Vlierberghe, E.V. (Eline Van), Vyvere, T.V. (Thijs vande), Vanhaudenhuyse, A. (Audrey), Vargiolu, A. (Alessia), Vega, E. (Emmanuel), Verheyden, J. (Jan), Vespa, P.M. (Paul M.), Vik, A. (Anne), Vilcinis, R. (Rimantas), Vizzino, G. (Giacinta), Vleggeert-Lankamp, C.L.A.M. (Carmen), Vulekovic, P. (Peter), Vámos, Z. (Zoltán), Wade, D. (Derick), Wang, K.K.W. (Kevin K.W.), Wang, L. (Lei), Wildschut, E.D. (Enno), Williams, G. (Guy), Willumsen, L. (Lisette), Wilson, A. (Adam), Wilson, L. (Lindsay), Winkler, M.K.L. (Maren K.L.), Ylén, P. (Peter), Younsi, A. (Alexander), Zaaroor, M. (Menashe), Zhang, Z. (Zhiqun), Zheng, Z. (Zelong), Zumbo, F. (Fabrizio), de Lange, S. (Stefanie), De Ruiter, G.C.W. (Godard C.W.), den Boogert, H. (Hugo), van Dijck, J. (Jeroen), Essen, T.A. (T.) van, van Heugten, C.M. (Caroline M.), Jagt, M. (Mathieu) van der, and Naalt, J. (Joukje) van der
Abstract: Background: No definitive evidence exists on how intracranial hypertension should be treated in patients with traumatic brain injury (TBI). It is therefore likely that centers and practitioners individually balance potential benefits and risks of different intracranial pressure (ICP) management strategies, resulting in practice variation. The aim of this study was to examine variation in monitoring and treatment policies for intracranial hypertension in patients with TBI. Methods: A 29-item survey on ICP monitoring and treatment was developed on the basis of literature and expert opinion, and it was pilot-tested in 16 centers. The questionnaire was sent to 68 neurotrauma centers participating in the Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Results: The survey was completed by 66 centers (97% response rate). Centers were mainly academic hospitals (n=60, 91%) and designated level I trauma centers (n=44, 67%). The Brain Trauma Foundation guidelines were used in 49 (74%) centers. Approximately 90% of the participants (n=58) indicated placing an ICP monitor in patients with severe TBI and computed tomographic abnormalities. There was no consensus on other indications or on peri-insertion precautions. We found wide variation in the use of first- and second-tier treatments for elevated ICP. Approximately half of the centers were classified as using a relatively aggressive approach to ICP monitoring and treatment (n=32, 48%), whereas the others were considered more conservative (n=34, 52%). Conclusions: Substantial variation was found regarding monitoring and treatment policies in patients with TBI and intracranial hypertension. The results of this survey indicate a lack of consensus between European neurotrauma centers and provide an opportunity and necessity for comparative effectiveness research.
Published: 2017
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21. DeepCut: object segmentation from bounding box annotations using convolutional neural networks

Author: Rajchl, M, Lee, M, Oktay, O, Kamnitsas, K, Passerat-Palmbach, J, Bai, W, Kainz, B, Rueckert, D, and Wellcome Trust/EPSRC
Abstract: In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations. It extends the approach of the well-known GrabCut method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naive approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.
Published: 2016

22. Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI

Author: Kamnitsas, K, Chen, L, Ledig, C, Rueckert, D, and Glocker, B
Published: 2015

23. Segmentation of Traumatic Brain Injuries with Convolutional Neural Networks

Author: Kamnitsas, K, Ledig, C, Newcombe, VFJ, Simpson, JP, Kane, AD, Menon, DK, Rueckert, D, and Glocker, B
Published: 2015

24. Variation in structure and process of care in traumatic brain injury: Provider profiles of European Neurotrauma Centers participating in the CENTER-TBI study

Author: Cnossen, M.C. (Maryse), Polinder, S. (Suzanne), Lingsma, H.F. (Hester F.), Maas, A.I.R. (Andrew), Menon, D.K. (David ), Steyerberg, E.W. (Ewout), Adams, H. (Hadie), Alessandro, M. (Masala), Allanson, J.E. (Judith), Amrein, K. (Krisztina), Andaluz, N. (Norberto), Andelic, N. (Nada), Andrea, N. (Nanni), Andreassen, L. (Lasse), Anke, A. (Audny), Antoni, A. (Anna), Ardon, H. (Hilko), Audibert, G. (Gérard), Auslands, K. (Kaspars), Azouvi, P. (Philippe), Baciu, C. (Camelia), Bacon, A. (Andrew), Badenes, R. (Rafael), Baglin, T. (Trevor), Bartels, R. (Ronald), Barzó, P. (Pál), Bauerfeind, U. (Ursula), Beer, R. (Ronny), Belda, F.J. (Francisco Javier), Bellander, B.-M. (Bo-Michael), Belli, A. (Antonio), Bellier, R. (Rémy), Benali, H. (Habib), Benard, T. (Thierry), Berardino, M. (Maurizio), Beretta, L. (Luigi), Beynon, C. (Christopher), Bilotta, F. (Federico), Binder, H. (Harald), Biqiri, E. (Erta), Blaabjerg, M. (Morten), Borgen, L.S. (Lund Stine), Bouzat, P. (Pierre), Bragge, P. (Peter), Brazinova, A. (Alexandra), Brehar, F. (Felix), Brorsson, C. (Camilla), Buki, A. (Andras), Bullinger, M. (Monika), Bučková, V. (Veronika), Calappi, E. (Emiliana), Cameron, P. (Peter), Carbayo, L.G. (Lozano Guillermo), Carise, E. (Elsa), Carpenter, C., Castaño-León, A.M. (Ana M.), Causin, F. (Francesco), Chevallard, G. (Giorgio), Chieregato, A. (Arturo), Citerio, G. (Giuseppe), Coburn, M. (Mark), Coles, J.P. (Jonathan P.), Cooper, J.D. (Jamie D.), Correia, M. (Marta), Covic, A. (Amra), Curry, N. (Nicola), Czeiter, E. (Endre), Czosnyka, M. (Marek), Dahyot-Fizelier, C. (Claire), Damas, F. (François), Damas, P. (Pierre), Dawes, H. (Helen), De Keyser, V. (Véronique), Della Corte, F. (Francesco), Depreitere, B. (Bart), Ding, S. (Shenghao), Dippel, D.W.J. (Diederik), Dizdarevic, K. (Kemal), Dulière, G.-L. (Guy-Loup), Dzeko, A. (Adelaida), Eapen, G. (George), Engemann, H. (Heiko), Ercole, A. (Ari), Esser, P. (Patrick), Ezer, E. (Erzsébet), Fabricius, M. (Martin), Feigin, V.L. (V.), Feng, J. (Junfeng), Foks, K. (Kelly), Fossi, F. (Francesca), Francony, G. (Gilles), Frantzén, J. (Janek), Freo, U. (Ulderico), Frisvold, S.K. (Shirin Kordasti), Furmanov, A. (Alex), Gagliardo, P. (Pablo), Galanaud, D. (Damien), Gao, G. (Guoyi), Geleijns, K. (Karin), Ghuysen, A. (Alexandre), Giraud, B. (Benoit), Glocker, B. (Ben), Gomez, P.A. (Pedro A.), Grossi, F. (Francesca), Gruen, R.L. (Russell), Gupta, D. (Deepak), Haagsma, J.A. (Juanita), Hadzic, E. (Ermin), Haitsma, I. (Iain), Hartings, J.A. (Jed), Helbok, R. (Raimund), Helseth, E. (Eirik), Hertle, D. (Daniel), Hill, S. (Sean), Hoedemaekers, A. (Astrid), Hoefer, S. (Stefan), Hutchinson, P.J. (Peter J.), Håberg, A.K. (Asta Kristine), Jacobs, B.C. (Bart), Janciak, I. (Ivan), Janssens, K. (Koen), Jiang, J.-Y. (Ji-Yao), Jones, K. (Kelly), Kalala, J.-P. (Jean-Pierre), Kamnitsas, K. (Konstantinos), Karan, M. (Mladen), Karau, J. (Jana), Katila, A. (Ari), Kaukonen, M. (Maija), Keeling, D. (David), Kerforne, T. (Thomas), Ketharanathan, N. (Naomi), Kettunen, J. (Johannes), Kivisaari, R. (Riku), Kolias, A.G. (Angelos G.), Kolumbán, B. (Bálint), Kompanje, E.J.O. (Erwin), Kondziella, D. (Daniel), Koskinen, L.-O. (Lars-Owe), Kovács, N. (Noémi), Kalovits, F. (Ferenc), Lagares, A. (Alfonso), Lanyon, L. (Linda), Laureys, S. (Steven), Lauritzen, M. (Martin), Lecky, F.E. (Fiona), Ledig, C. (Christian), Lefering, R., Legrand, V. (Valerie), Lei, J. (Jin), Levi, L. (Leon), Lightfoot, R. (Roger), Lingsma, H.F. (Hester), Loeckx, D. (Dirk), Lozano, A. (Angels), Luddington, R. (Roger), Luijten-Arts, C. (Chantal), MacDonald, S. (Stephen), MacFayden, C. (Charles), Maegele, M. (Marc), Majdan, M. (Marek), Major, S. (Sebastian), Manara, A. (Alex), Manhes, P. (Pauline), Manley, G. (Geoffrey), Martin, D. (Didier), Martino, C. (Costanza), Maruenda, A. (Armando), Maréchal, H. (Hugues), Mastelova, D. (Dagmara), Mattern, J. (Julia), McMahon, C. (Catherine), Melegh, B. (Béla), Menovsky, T. (Tomas), Morganti-Kossmann, C. (Cristina), Mulazzi, D. (Davide), Mutschler, M. (Manuel), Mühlan, H. (Holger), Negru, A. (Ancuta), Nelson, D. (David), Neugebauer, E. (Eddy), Newcombe, V.F. (Virginia F.), Noirhomme, Q. (Quentin), Nyirádi, J. (József), Oddo, M. (Mauro), Oldenbeuving, A. (Annemarie), Oresic, M. (Matej), Ortolano, F. (Fabrizio), Palotie, A. (Aarno), Parizel, P.M., Patruno, A. (Adriana), Payen, J.-F. (Jean-François), Perera, N. (Natascha), Perlbarg, V. (Vincent), Persona, P. (Paolo), Peul, W.C. (Wilco), Pichon, N. (Nicolas), Piilgaard, H. (Henning), Piippo, A. (Anna), Pili, F.S. (Floury Sébastien), Pirinen, M. (Matti), Ples, H. (Horia), Pomposo, I. (Inigo), Psota, M. (Marek), Pullens, P. (Pim), Puybasset, L. (Louis), Ragauskas, A. (Arminas), Raj, R. (Rahul), Rambadagalla, M. (Malinka), Rehorčíková, V. (Veronika), Rhodes, J.K.J. (Jonathan K.J.), Richardson, S. (Sylvia), Ripatti, S. (Samuli), Rocka, S. (Saulius), Rodier, N. (Nicolas), Roe, C. (Cecilie), Roise, O. (Olav), Roks, G. (Gerwin), Romegoux, P. (Pauline), Rosand, J. (Jonathan), Rosenfeld, J. (Jeffrey), Rosenlund, C. (Christina), Rosenthal, G. (Guy), Rossaint, R. (Rolf), Rossi, S. (Sandra), Rostalski, T. (Tim), Rueckert, D.L. (Danie L.), Ruiz De Arcaute, F. (Felix), Rusnák, M. (Martin), Sacchi, M. (Marco), Sahakian, B. (Barbara), Sahuquillo, J. (Juan), Sakowitz, O. (Oliver), Sala, F. (Francesca), Sanchez-Pena, P. (Paola), Sanchez-Porras, R. (Renan), Sandor, J. (Janos), Santos, E. (Edgar), Sasse, N. (Nadine), Sasu, L. (Luminita), Savo, D. (Davide), Schipper, I.B. (Inger), Schlößer, B. (Barbara), Schmidt, S. (Silke), Schneider, A. (Annette), Schoechl, H. (Herbert), Schoonman, G.G. (Guus), Schou, R. (Rico), Schwendenwein, E. (Elisabeth), Schöll, M. (Michael), Sir, O. (Özcan), Skandsen, T. (Toril), Smakman, L. (Lidwien), Smeets, D. (Dirk), Smielewski, P. (Peter), Sorinola, A. (Abayomi), Stamatakis, E.L. (Emmanue L.), Stanworth, S. (Simon), Stegemann, K. (Katrin), Steinbüchel, N. (Nicole), Stevens, R. (Robert), Stewart, W. (William), Stocchetti, N. (Nino), Sundström, N. (Nina), Synnot, A. (Anneliese), Szabó, J. (József), Söderberg, J. (Jeannette), Taccone, F.S. (Fabio), Tamás, V. (Viktória), Tanskanen, P. (Päivi), Tascu, A. (Alexandru), Taylor, M.S. (Mark Steven), Te Ao, B. (Braden), Tenovuo, O. (Olli), Teodorani, G. (Guido), Theadom, A. (Alice), Thomas, M. (Matt), Tibboel, D. (Dick), Tolias, C.M. (Christos M.), Tshibanda, J.-F.L. (Jean-Flory Luaba), Tudora, C.M. (Cristina Maria), Vajkoczy, P. (Peter), Valeinis, E. (Egils), Hecke, W. (Wim) van, Van Praag, D. (Dominique), Van Roost, D. (Dirk), Van Vlierberghe, E. (Eline), Vande Vyvere, T. (Thijs), Vanhaudenhuyse, A. (Audrey), Vargiolu, A. (Alessia), Vega, E. (Emmanuel), Verheyden, J. (Jan), Vespa, P.M. (Paul M.), Vik, A. (Anne), Vilcinis, R. (Rimantas), Vizzino, G. (Giacinta), Vleggeert-Lankamp, C.L.A.M. (Carmen), Volovici, V. (Victor), Vulekovic, P. (Peter), Vámos, Z. (Zoltán), Wade, D. (Derick), Wang, K.K.W. (Kevin K.W.), Wang, L. (Lei), Wildschut, E. (Eno), Williams, G. (Guy), Willumsen, L. (Lisette), Wilson, A. (Adam), Wilson, L. (Lindsay), Winkler, M.K.L. (Maren K. L.), Ylén, P. (Peter), Younsi, A. (Alexander), Zaaroor, M. (Menashe), Zhang, Z. (Zhiqun), Zheng, Z. (Zelong), Zumbo, F. (Fabrizio), De Lange, S. (Stefanie), De Ruiter, G.C.W. (Godard C.W.), Den Boogert, H. (Hugo), Van Dijck, J. (Jeroen), Essen, T.A. (T.) van, van Heugten, C.M. (Caroline M.), Jagt, M. (Mathieu) van der, Naalt, J. (Joukje) van der, Cnossen, M.C. (Maryse), Polinder, S. (Suzanne), Lingsma, H.F. (Hester F.), Maas, A.I.R. (Andrew), Menon, D.K. (David ), Steyerberg, E.W. (Ewout), Adams, H. (Hadie), Alessandro, M. (Masala), Allanson, J.E. (Judith), Amrein, K. (Krisztina), Andaluz, N. (Norberto), Andelic, N. (Nada), Andrea, N. (Nanni), Andreassen, L. (Lasse), Anke, A. (Audny), Antoni, A. (Anna), Ardon, H. (Hilko), Audibert, G. (Gérard), Auslands, K. (Kaspars), Azouvi, P. (Philippe), Baciu, C. (Camelia), Bacon, A. (Andrew), Badenes, R. (Rafael), Baglin, T. (Trevor), Bartels, R. (Ronald), Barzó, P. (Pál), Bauerfeind, U. (Ursula), Beer, R. (Ronny), Belda, F.J. (Francisco Javier), Bellander, B.-M. (Bo-Michael), Belli, A. (Antonio), Bellier, R. (Rémy), Benali, H. (Habib), Benard, T. (Thierry), Berardino, M. (Maurizio), Beretta, L. (Luigi), Beynon, C. (Christopher), Bilotta, F. (Federico), Binder, H. (Harald), Biqiri, E. (Erta), Blaabjerg, M. (Morten), Borgen, L.S. (Lund Stine), Bouzat, P. (Pierre), Bragge, P. (Peter), Brazinova, A. (Alexandra), Brehar, F. (Felix), Brorsson, C. (Camilla), Buki, A. (Andras), Bullinger, M. (Monika), Bučková, V. (Veronika), Calappi, E. (Emiliana), Cameron, P. (Peter), Carbayo, L.G. (Lozano Guillermo), Carise, E. (Elsa), Carpenter, C., Castaño-León, A.M. (Ana M.), Causin, F. (Francesco), Chevallard, G. (Giorgio), Chieregato, A. (Arturo), Citerio, G. (Giuseppe), Coburn, M. (Mark), Coles, J.P. (Jonathan P.), Cooper, J.D. (Jamie D.), Correia, M. (Marta), Covic, A. (Amra), Curry, N. (Nicola), Czeiter, E. (Endre), Czosnyka, M. (Marek), Dahyot-Fizelier, C. (Claire), Damas, F. (François), Damas, P. (Pierre), Dawes, H. (Helen), De Keyser, V. (Véronique), Della Corte, F. (Francesco), Depreitere, B. (Bart), Ding, S. (Shenghao), Dippel, D.W.J. (Diederik), Dizdarevic, K. (Kemal), Dulière, G.-L. (Guy-Loup), Dzeko, A. (Adelaida), Eapen, G. (George), Engemann, H. (Heiko), Ercole, A. (Ari), Esser, P. (Patrick), Ezer, E. (Erzsébet), Fabricius, M. (Martin), Feigin, V.L. (V.), Feng, J. (Junfeng), Foks, K. (Kelly), Fossi, F. (Francesca), Francony, G. (Gilles), Frantzén, J. (Janek), Freo, U. (Ulderico), Frisvold, S.K. (Shirin Kordasti), Furmanov, A. (Alex), Gagliardo, P. (Pablo), Galanaud, D. (Damien), Gao, G. (Guoyi), Geleijns, K. (Karin), Ghuysen, A. (Alexandre), Giraud, B. (Benoit), Glocker, B. (Ben), Gomez, P.A. (Pedro A.), Grossi, F. (Francesca), Gruen, R.L. (Russell), Gupta, D. (Deepak), Haagsma, J.A. (Juanita), Hadzic, E. (Ermin), Haitsma, I. (Iain), Hartings, J.A. (Jed), Helbok, R. (Raimund), Helseth, E. (Eirik), Hertle, D. (Daniel), Hill, S. (Sean), Hoedemaekers, A. (Astrid), Hoefer, S. (Stefan), Hutchinson, P.J. (Peter J.), Håberg, A.K. (Asta Kristine), Jacobs, B.C. (Bart), Janciak, I. (Ivan), Janssens, K. (Koen), Jiang, J.-Y. (Ji-Yao), Jones, K. (Kelly), Kalala, J.-P. (Jean-Pierre), Kamnitsas, K. (Konstantinos), Karan, M. (Mladen), Karau, J. (Jana), Katila, A. (Ari), Kaukonen, M. (Maija), Keeling, D. (David), Kerforne, T. (Thomas), Ketharanathan, N. (Naomi), Kettunen, J. (Johannes), Kivisaari, R. (Riku), Kolias, A.G. (Angelos G.), Kolumbán, B. (Bálint), Kompanje, E.J.O. (Erwin), Kondziella, D. (Daniel), Koskinen, L.-O. (Lars-Owe), Kovács, N. (Noémi), Kalovits, F. (Ferenc), Lagares, A. (Alfonso), Lanyon, L. (Linda), Laureys, S. (Steven), Lauritzen, M. (Martin), Lecky, F.E. (Fiona), Ledig, C. (Christian), Lefering, R., Legrand, V. (Valerie), Lei, J. (Jin), Levi, L. (Leon), Lightfoot, R. (Roger), Lingsma, H.F. (Hester), Loeckx, D. (Dirk), Lozano, A. (Angels), Luddington, R. (Roger), Luijten-Arts, C. (Chantal), MacDonald, S. (Stephen), MacFayden, C. (Charles), Maegele, M. (Marc), Majdan, M. (Marek), Major, S. (Sebastian), Manara, A. (Alex), Manhes, P. (Pauline), Manley, G. (Geoffrey), Martin, D. (Didier), Martino, C. (Costanza), Maruenda, A. (Armando), Maréchal, H. (Hugues), Mastelova, D. (Dagmara), Mattern, J. (Julia), McMahon, C. (Catherine), Melegh, B. (Béla), Menovsky, T. (Tomas), Morganti-Kossmann, C. (Cristina), Mulazzi, D. (Davide), Mutschler, M. (Manuel), Mühlan, H. (Holger), Negru, A. (Ancuta), Nelson, D. (David), Neugebauer, E. (Eddy), Newcombe, V.F. (Virginia F.), Noirhomme, Q. (Quentin), Nyirádi, J. (József), Oddo, M. (Mauro), Oldenbeuving, A. (Annemarie), Oresic, M. (Matej), Ortolano, F. (Fabrizio), Palotie, A. (Aarno), Parizel, P.M., Patruno, A. (Adriana), Payen, J.-F. (Jean-François), Perera, N. (Natascha), Perlbarg, V. (Vincent), Persona, P. (Paolo), Peul, W.C. (Wilco), Pichon, N. (Nicolas), Piilgaard, H. (Henning), Piippo, A. (Anna), Pili, F.S. (Floury Sébastien), Pirinen, M. (Matti), Ples, H. (Horia), Pomposo, I. (Inigo), Psota, M. (Marek), Pullens, P. (Pim), Puybasset, L. (Louis), Ragauskas, A. (Arminas), Raj, R. (Rahul), Rambadagalla, M. (Malinka), Rehorčíková, V. (Veronika), Rhodes, J.K.J. (Jonathan K.J.), Richardson, S. (Sylvia), Ripatti, S. (Samuli), Rocka, S. (Saulius), Rodier, N. (Nicolas), Roe, C. (Cecilie), Roise, O. (Olav), Roks, G. (Gerwin), Romegoux, P. (Pauline), Rosand, J. (Jonathan), Rosenfeld, J. (Jeffrey), Rosenlund, C. (Christina), Rosenthal, G. (Guy), Rossaint, R. (Rolf), Rossi, S. (Sandra), Rostalski, T. (Tim), Rueckert, D.L. (Danie L.), Ruiz De Arcaute, F. (Felix), Rusnák, M. (Martin), Sacchi, M. (Marco), Sahakian, B. (Barbara), Sahuquillo, J. (Juan), Sakowitz, O. (Oliver), Sala, F. (Francesca), Sanchez-Pena, P. (Paola), Sanchez-Porras, R. (Renan), Sandor, J. (Janos), Santos, E. (Edgar), Sasse, N. (Nadine), Sasu, L. (Luminita), Savo, D. (Davide), Schipper, I.B. (Inger), Schlößer, B. (Barbara), Schmidt, S. (Silke), Schneider, A. (Annette), Schoechl, H. (Herbert), Schoonman, G.G. (Guus), Schou, R. (Rico), Schwendenwein, E. (Elisabeth), Schöll, M. (Michael), Sir, O. (Özcan), Skandsen, T. (Toril), Smakman, L. (Lidwien), Smeets, D. (Dirk), Smielewski, P. (Peter), Sorinola, A. (Abayomi), Stamatakis, E.L. (Emmanue L.), Stanworth, S. (Simon), Stegemann, K. (Katrin), Steinbüchel, N. (Nicole), Stevens, R. (Robert), Stewart, W. (William), Stocchetti, N. (Nino), Sundström, N. (Nina), Synnot, A. (Anneliese), Szabó, J. (József), Söderberg, J. (Jeannette), Taccone, F.S. (Fabio), Tamás, V. (Viktória), Tanskanen, P. (Päivi), Tascu, A. (Alexandru), Taylor, M.S. (Mark Steven), Te Ao, B. (Braden), Tenovuo, O. (Olli), Teodorani, G. (Guido), Theadom, A. (Alice), Thomas, M. (Matt), Tibboel, D. (Dick), Tolias, C.M. (Christos M.), Tshibanda, J.-F.L. (Jean-Flory Luaba), Tudora, C.M. (Cristina Maria), Vajkoczy, P. (Peter), Valeinis, E. (Egils), Hecke, W. (Wim) van, Van Praag, D. (Dominique), Van Roost, D. (Dirk), Van Vlierberghe, E. (Eline), Vande Vyvere, T. (Thijs), Vanhaudenhuyse, A. (Audrey), Vargiolu, A. (Alessia), Vega, E. (Emmanuel), Verheyden, J. (Jan), Vespa, P.M. (Paul M.), Vik, A. (Anne), Vilcinis, R. (Rimantas), Vizzino, G. (Giacinta), Vleggeert-Lankamp, C.L.A.M. (Carmen), Volovici, V. (Victor), Vulekovic, P. (Peter), Vámos, Z. (Zoltán), Wade, D. (Derick), Wang, K.K.W. (Kevin K.W.), Wang, L. (Lei), Wildschut, E. (Eno), Williams, G. (Guy), Willumsen, L. (Lisette), Wilson, A. (Adam), Wilson, L. (Lindsay), Winkler, M.K.L. (Maren K. L.), Ylén, P. (Peter), Younsi, A. (Alexander), Zaaroor, M. (Menashe), Zhang, Z. (Zhiqun), Zheng, Z. (Zelong), Zumbo, F. (Fabrizio), De Lange, S. (Stefanie), De Ruiter, G.C.W. (Godard C.W.), Den Boogert, H. (Hugo), Van Dijck, J. (Jeroen), Essen, T.A. (T.) van, van Heugten, C.M. (Caroline M.), Jagt, M. (Mathieu) van der, and Naalt, J. (Joukje) van der
Abstract: Introduction: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Methods: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. Results: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. Conclusion: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches.
Published: 2016
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25. Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty

Author: Monteiro, M., Loïc Le Folgoc, Castro, D. C., Pawlowski, N., Marques, B., Kamnitsas, K., Wilk, M., Glocker, B., Engineering & Physical Science Research Council (E, and Commission of the European Communities
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), cs.LG, Computer Science - Computer Vision and Pattern Recognition, cs.CV, Machine Learning (cs.LG)
Abstract: In image segmentation, there is often more than one plausible solution for a given input. In medical imaging, for example, experts will often disagree about the exact location of object boundaries. Estimating this inherent uncertainty and predicting multiple plausible hypotheses is of great interest in many applications, yet this ability is lacking in most current deep learning methods. In this paper, we introduce stochastic segmentation networks (SSNs), an efficient probabilistic method for modelling aleatoric uncertainty with any image segmentation network architecture. In contrast to approaches that produce pixel-wise estimates, SSNs model joint distributions over entire label maps and thus can generate multiple spatially coherent hypotheses for a single image. By using a low-rank multivariate normal distribution over the logit space to model the probability of the label map given the image, we obtain a spatially consistent probability distribution that can be efficiently computed by a neural network without any changes to the underlying architecture. We tested our method on the segmentation of real-world medical data, including lung nodules in 2D CT and brain tumours in 3D multimodal MRI scans. SSNs outperform state-of-the-art for modelling correlated uncertainty in ambiguous images while being much simpler, more flexible, and more efficient., Comment: Published at Neurips2020. 17 pages, 11 figures, 2 tables

26. Quantitative histology for assessing organ health

Author: Tam, KH, Boor, P, Kamnitsas, K, Ploeg, R, Sirinukunwattana, K, Kaisar, M, and Rittscher, J
Subjects: Transplantation of organs, tissues, etc, Deep learning (Machine learning), Tissue banks, Computer vision, Biomedical engineering, Image analysis
Abstract: This thesis aims to take the first step in developing a quantitative analysis workflow for characterising histological changes in pre-implantation renal biopsies. Solving this is highly beneficial as there is a significant disparity between organ supply and demand worldwide. It can also help to alleviate the workload caused by a shortage of pathologists. This work would allow quantitative measurement of histological features related to chronic and acute injuries in pre-implantation biopsies. These features can then be linked to scores given by pathologists, post-transplant clinical endpoints, and proteomic biomarkers to help researchers better understand the mechanisms of organ injury and repair. First, we build the main feature extraction workflow based on convolutional neural networks (CNN) to automatically segment different tissue compartments in renal biopsies. We design handcrafted features and qualitatively show that some of these features can capture visual changes related to chronic and acute injuries. Then, we find that many tissue compartments in the primary dataset have artefacts not present in the training data. We discuss Bayesian Neural Networks (BNNs) to quantify uncertainties in segmentation and propose a way to improve histological estimates. Next, we propose using tissue compartment-based features to improve classification and interpretability in a multi-instance learning setting. In addition to learned attention, tissue compartments are weighted by segmentation quality to reduce the impact of instances that bear little resemblance to the training data. Finally, we combine techniques developed in this project to detect acute injuries from biopsies. We propose an algorithmic score consisting of models trained on pathologists’ assessments and donors’ clinical states. The score is significantly associated with Delayed Graft Function (DGF) in the test data. These findings suggest that our workflow can facilitate research in kidney transplant where pathologist time is limited. It also shows histology has great potential in predicting post-transplant complications.
Published: 2023

27. Relationship between Measures of Cerebrovascular Reactivity and Intracranial Lesion Progression in Acute Traumatic Brain Injury Patients: A CENTER-TBI Study

Author: Ari Ercole, Virginia F. J. Newcombe, Frederick A. Zeiler, Peter J. Hutchinson, Marek Czosnyka, David K. Menon, Investigators, Peter Smielewski, Miguel Monteiro, Konstantinos Kamnitsas, Francois Mathieu, Ben Glocker, Tilak Das, Daniel P. Whitehouse, Mathieu, F, Zeiler, F, Ercole, A, Monteiro, M, Kamnitsas, K, Glocker, B, Whitehouse, D, Das, T, Smielewski, P, Czosnyka, M, Hutchinson, P, Newcombe, V, Menon, D, and Citerio, G
Subjects: Adult, Male, 030506 rehabilitation, medicine.medical_specialty, Traumatic brain injury, Cerebral autoregulation, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Cerebrovascular reactivity, Internal medicine, Edema, Brain Injuries, Traumatic, Medicine, Humans, Cerebral perfusion pressure, Intraparenchymal hemorrhage, Intersectoral Collaboration, cerebral autoregulation, intracranial hemorrhage, traumatic brain injury, business.industry, Original Articles, Middle Aged, medicine.disease, Europe, Intensive Care Units, Treatment Outcome, Brain Injuries, Cerebrovascular Circulation, Cohort, Cardiology, Disease Progression, Intracranial lesions, Female, Neurology (clinical), medicine.symptom, 0305 other medical science, business, Tomography, X-Ray Computed, 030217 neurology & neurosurgery
Abstract: Failure of cerebral autoregulation has been linked to unfavorable outcome after traumatic brain injury (TBI). Preliminary evidence from a small, retrospective, single-center analysis suggests that autoregulatory dysfunction may be associated with traumatic lesion expansion, particularly for pericontusional edema. The goal of this study was to further explore these associations using prospective, multi-center data from the Collaborative European Neurotrauma Effectiveness Research in TBI (CENTER-TBI) and to further explore the relationship between autoregulatory failure, lesion progression, and patient outcome. A total of 88 subjects from the CENTER-TBI High Resolution ICU Sub-Study cohort were included. All patients had an admission computed tomography (CT) scan and early repeat scan available, as well as high-frequency neurophysiological recordings covering the between-scan interval. Using a novel, semiautomated approach at lesion segmentation, we calculated absolute changes in volume of contusion core, pericontusional edema, and extra-axial hemorrhage between the imaging studies. We then evaluated associations between cerebrovascular reactivity metrics and radiological lesion progression using mixed-model regression. Analyses were adjusted for baseline covariates and non-neurophysiological factors associated with lesion growth using multi-variate methods. Impairment in cerebrovascular reactivity was significantly associated with progression of pericontusional edema and, to a lesser degree, intraparenchymal hemorrhage. In contrast, there were no significant associations with extra-axial hemorrhage. The strongest relationships were observed between RAC-based metrics and edema formation. Pulse amplitude index showed weaker, but consistent, associations with contusion growth. Cerebrovascular reactivity metrics remained strongly associated with lesion progression after taking into account contributions from non-neurophysiological factors and mean cerebral perfusion pressure. Total hemorrhagic core and edema volumes on repeat CT were significantly larger in patients who were deceased at 6 months, and the amount of edema was greater in patients with an unfavourable outcome (Glasgow Outcome Scale-Extended 1-4). Our study suggests associations between autoregulatory failure, traumatic edema progression, and poor outcome. This is in keeping with findings from a single-center retrospective analysis, providing multi-center prospective data to support those results.
Published: 2020

28. Variation in monitoring and treatment policies for intracranial hypertension in traumatic brain injury

Author: Cnossen, Maryse C., Huijben, Jilske A., van der Jagt, Mathieu, Volovici, Victor, van Essen, Thomas, Polinder, Suzanne, Nelson, David, Ercole, Ari, Stocchetti, Nino, Citerio, Giuseppe, Peul, Wilco C., Maas, Andrew I. R., Menon, David, Steyerberg, Ewout W., Lingsma, Hester F., Adams, Hadie, Alessandro, Masala, Allanson, Judith, Amrein, Krisztina, Andaluz, Norberto, Andelic, Nada, Andrea, Nanni, Andreassen, Lasse, Anke, Audny, Antoni, Anna, Ardon, Hilko, Audibert, Gérard, Auslands, Kaspars, Azouvi, Philippe, Baciu, Camelia, Bacon, Andrew, Badenes, Rafael, Baglin, Trevor, Bartels, Ronald, Barzó, Pál, Bauerfeind, Ursula, Beer, Ronny, Belda, Francisco Javier, Bellander, Bo-Michael, Belli, Antonio, Bellier, Rémy, Benali, Habib, Benard, Thierry, Berardino, Maurizio, Beretta, Luigi, Beynon, Christopher, Bilotta, Federico, Binder, Harald, Biqiri, Erta, Blaabjerg, Morten, Lund, Stine Borgen, Bouzat, Pierre, Bragge, Peter, Brazinova, Alexandra, Brehar, Felix, Brorsson, Camilla, Buki, Andras, Bullinger, Monika, Bucková, Veronika, Calappi, Emiliana, Cameron, Peter, Carbayo, Lozano Guillermo, Carise, Elsa, Carpenter, Keri, Castaño-León, Ana M., Causin, Francesco, Chevallard, Giorgio, Chieregato, Arturo, Cooper, M., Coburn, Mark, Coles, Jonathan, Cooper, Jamie D., Correia, Marta, Covic, Amra, Curry, Nicola, Czeiter, Endre, Czosnyka, Marek, Dahyot-Fizelier, Claire, Damas, François, Damas, Pierre, Dawes, Helen, De Keyser, Véronique, Corte, Francesco Della, Depreitere, Bart, Ding, Shenghao, Dippel, Diederik, Dizdarevic, Kemal, Dulière, Guy-Loup, Dzeko, Adelaida, Eapen, George, Engemann, Heiko, Esser, Patrick, Ezer, Erzsébet, Fabricius, Martin, Feigin, Valery L., Feng, Junfeng, Foks, Kelly, Fossi, Francesca, Francony, Gilles, Frantzén, Janek, Freo, Ulderico, Frisvold, Shirin, Furmanov, Alex, Gagliardo, Pablo, Galanaud, Damien, Gao, Guoyi, Geleijns, Karin, Ghuysen, Alexandre, Giraud, Benoit, Glocker, Ben, Gomez, Pedro A., Grossi, Francesca, Gruen, Russell L., Gupta, Deepak, Haagsma, Juanita A., Hadzic, Ermin, Haitsma, Iain, Hartings, Jed A., Helbok, Raimund, Helseth, Eirik, Hertle, Daniel, Hill, Sean, Hoedemaekers, Astrid, Hoefer, Stefan, Hutchinson, Peter J., Håberg, Kristine Asta, Jacobs, Bram, Janciak, Ivan, Janssens, Koen, Jiang, Ji-Yao, Jones, Kelly, Kalala, Jean-Pierre, Kamnitsas, Konstantinos, Karan, Mladen, Karau, Jana, Katila, Ari, Kaukonen, Maija, Keeling, David, Kerforne, Thomas, Ketharanathan, Naomi, Kettunen, Johannes, Kivisaari, Riku, Kolias, Angelos G., Kolumbán, Bálint, Kompanje, Erwin, Kondziella, Daniel, Koskinen, Lars-Owe, Kovács, Noémi, Kálovits, Ferenc, Lagares, Alfonso, Lanyon, Linda, Laureys, Steven, Lauritzen, Martin, Lecky, Fiona, Ledig, Christian, Lefering, Rolf, Legrand, Valerie, Lei, Jin, Levi, Leon, Lightfoot, Roger, Loeckx, Dirk, Lozano, Angels, Luddington, Roger, Luijten-Arts, Chantal, Macdonald, Stephen, Macfayden, Charles, Maegele, Marc, Majdan, Marek, Major, Sebastian, Manara, Alex, Manhes, Pauline, Manley, Geoffrey, Martin, Didier, Martino, Costanza, Maruenda, Armando, Maréchal, Hugues, Mastelova, Dagmara, Mattern, Julia, Mcmahon, Catherine, Melegh, Béla, Menovsky, Tomas, Morganti-Kossmann, Cristina, Mulazzi, Davide, Mutschler, Manuel, Mühlan, Holger, Negru, Ancuta, Neugebauer, Eddy, Newcombe, Virginia, Noirhomme, Quentin, Nyirádi, József, Oddo, Mauro, Oldenbeuving, Annemarie, Oresic, Matej, Ortolano, Fabrizio, Palotie, Aarno, Parizel, Paul M., Patruno, Adriana, Payen, Jean-François, Perera, Natascha, Perlbarg, Vincent, Persona, Paolo, Pichon, Nicolas, Piilgaard, Henning, Piippo, Anna, Floury, Sébastien Pili, Pirinen, Matti, Ples, Horia, Pomposo, Inigo, Psota, Marek, Pullens, Pim, Puybasset, Louis, Ragauskas, Arminas, Raj, Rahul, Rambadagalla, Malinka, Rehorcíková, Veronika, Rhodes, Jonathan, Richardson, Sylvia, Ripatti, Samuli, Rocka, Saulius, Rodier, Nicolas, Roe, Cecilie, Roise, Olav, Roks, Gerwin, Romegoux, Pauline, Rosand, Jonathan, Rosenfeld, Jeffrey, Rosenlund, Christina, Rosenthal, Guy, Rossaint, Rolf, Rossi, Sandra, Rostalski, Tim, Rueckert, Daniel, de Ruiz, Arcaute Felix, Rusnák, Martin, Sacchi, Marco, Sahakian, Barbara, Sahuquillo, Juan, Sakowitz, Oliver, Sala, Francesca, Sanchez-Pena, Paola, Sanchez-Porras, Renan, Sandor, Janos, Santos, Edgar, Sasse, Nadine, Sasu, Luminita, Savo, Davide, Schipper, Inger, Schlößer, Barbara, Schmidt, Silke, Schneider, Annette, Schoechl, Herbert, Schoonman, Guus, Rico, Frederik Schou, Schwendenwein, Elisabeth, Schöll, Michael, Sir, Özcan, Skandsen, Toril, Smakman, Lidwien, Smeets, Dirk, Smielewski, Peter, Sorinola, Abayomi, Stamatakis, Emmanuel, Stanworth, Simon, Stegemann, Katrin, Steinbüchel, Nicole, Stevens, Robert, Stewart, William, Sundström, Nina, Synnot, Anneliese, Szabó, József, Söderberg, Jeannette, Taccone, Fabio Silvio, Tamás, Viktória, Tanskanen, Päivi, Tascu, Alexandru, Taylor, Mark Steven, Ao Braden, Te, Tenovuo, Olli, Teodorani, Guido, Theadom, Alice, Thomas, Matt, Tibboel, Dick, Tolias, Christos, Tshibanda, Jean-Flory Luaba, Tudora, Cristina Maria, Vajkoczy, Peter, Valeinis, Egils, Hecke, Wim Van, Praag, Dominique Van, Dirk, Van Roost, Vlierberghe, Eline Van, Vyvere, Thijs vande, Vanhaudenhuyse, Audrey, Vargiolu, Alessia, Vega, Emmanuel, Verheyden, Jan, Vespa, Paul M., Vik, Anne, Vilcinis, Rimantas, Vizzino, Giacinta, Vleggeert-Lankamp, Carmen, Vulekovic, Peter, Vámos, Zoltán, Wade, Derick, Wang, Kevin K. W., Wang, Lei, Wildschut, Eno, Williams, Guy, Willumsen, Lisette, Wilson, Adam, Wilson, Lindsay, Winkler, Maren K. L., Ylén, Peter, Younsi, Alexander, Zaaroor, Menashe, Zhang, Zhiqun, Zheng, Zelong, Zumbo, Fabrizio, de Lange, Stefanie, de Ruiter, Godard C. W., den Boogert, Hugo, van Dijck, Jeroen, van Heugten, Caroline, van der Naalt, Joukje, Cnossen, Maryse C., Huijben, Jilske A., Van der Jagt, Mathieu, Volovici, Victor, Van Essen, Thoma, Polinder, Suzanne, Nelson, David, Ercole, Ari, Stocchetti, Nino, Citerio, Giuseppe, Peul, Wilco C., Maas, Andrew I. R., Menon, David, Steyerberg, Ewout W., Lingsma, Hester F., on behalf of the CENTER-TBI, Investigator, Beretta, Luigi, Cnossen, M, Huijben, J, van der Jagt, M, Volovici, V, van Essen, T, Polinder, S, Nelson, D, Ercole, A, Stocchetti, N, Citerio, G, Peul, W, Maas, A, Menon, D, Steyerberg, E, Lingsma, H, Molecular Neuroscience and Ageing Research (MOLAR), Psychiatrie & Neuropsychologie, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, Commission of the European Communities, CENTER-TBI Investigators, Medical Informatics, Public and occupational health, Ragauskas, Arminas, Ročka, Saulius, Vilcinis, Rimantas, Public Health, Intensive Care, Neurosurgery, CENTER-TBI investigators, Adams, H., Alessandro, M., Allanson, J., Amrein, K., Andaluz, N., Andelic, N., Andrea, N., Andreassen, L., Anke, A., Antoni, A., Ardon, H., Audibert, G., Auslands, K., Azouvi, P., Baciu, C., Bacon, A., Badenes, R., Baglin, T., Bartels, R., Barzó, P., Bauerfeind, U., Beer, R., Belda, F.J., Bellander, B.M., Belli, A., Bellier, R., Benali, H., Benard, T., Berardino, M., Beretta, L., Beynon, C., Bilotta, F., Binder, H., Biqiri, E., Blaabjerg, M., Borgen, L.S., Bouzat, P., Bragge, P., Brazinova, A., Brehar, F., Brorsson, C., Buki, A., Bullinger, M., Bučková, V., Calappi, E., Cameron, P., Lozano, G.C., Carise, E., Carpenter, K., Castaño-León, A.M., Causin, F., Chevallard, G., Chieregato, A., Citerio, G., Cnossen, M., Coburn, M., Coles, J., Cooper, J.D., Correia, M., Covic, A., Curry, N., Czeiter, E., Czosnyka, M., Dahyot-Fizelier, C., Damas, F., Damas, P., Dawes, H., De Keyser, V., Corte, F.D., Depreitere, B., Ding, S., Dippel, D., Dizdarevic, K., Dulière, G.L., Dzeko, A., Eapen, G., Engemann, H., Ercole, A., Esser, P., Ezer, E., Fabricius, M., Feigin, V.L., Feng, J., Foks, K., Fossi, F., Francony, G., Frantzén, J., Freo, U., Frisvold, S., Furmanov, A., Gagliardo, P., Galanaud, D., Gao, G., Geleijns, K., Ghuysen, A., Giraud, B., Glocker, B., Gomez, P.A., Grossi, F., Gruen, R.L., Gupta, D., Haagsma, J.A., Hadzic, E., Haitsma, I., Hartings, J.A., Helbok, R., Helseth, E., Hertle, D., Hill, S., Hoedemaekers, A., Hoefer, S., Hutchinson, P.J., Håberg, A.K., Jacobs, B., Janciak, I., Janssens, K., Jiang, J.Y., Jones, K., Kalala, J.P., Kamnitsas, K., Karan, M., Karau, J., Katila, A., Kaukonen, M., Keeling, D., Kerforne, T., Ketharanathan, N., Kettunen, J., Kivisaari, R., Kolias, A.G., Kolumbán, B., Kompanje, E., Kondziella, D., Koskinen, L.O., Kovács, N., Kálovits, F., Lagares, A., Lanyon, L., Laureys, S., Lauritzen, M., Lecky, F., Ledig, C., Lefering, R., Legrand, V., Lei, J., Levi, L., Lightfoot, R., Lingsma, H., Loeckx, D., Lozano, A., Luddington, R., Luijten-Arts, C., Andrew, IRM, MacDonald, S., MacFayden, C., Maegele, M., Majdan, M., Major, S., Manara, A., Manhes, P., Manley, G., Martin, D., Martino, C., Maruenda, A., Maréchal, H., Mastelova, D., Mattern, J., McMahon, C., Melegh, B., Menon, D., Menovsky, T., Morganti-Kossmann, C., Mulazzi, D., Mutschler, M., Mühlan, H., Negru, A., Nelson, D., Neugebauer, E., Newcombe, V., Noirhomme, Q., Nyirádi, J., Oddo, M., Oldenbeuving, A., Oresic, M., Ortolano, F., Palotie, A., Parizel, P.M., Patruno, A., Payen, J.F., Perera, N., Perlbarg, V., Persona, P., Peul, W., Pichon, N., Piilgaard, H., Piippo, A., Floury, S.P., Pirinen, M., Ples, H., Polinder, S., Pomposo, I., Psota, M., Pullens, P., Puybasset, L., Ragauskas, A., Raj, R., Rambadagalla, M., Rehorčíková, V., Rhodes, J., Richardson, S., Ripatti, S., Rocka, S., Rodier, N., Roe, C., Roise, O., Roks, G., Romegoux, P., Rosand, J., Rosenfeld, J., Rosenlund, C., Rosenthal, G., Rossaint, R., Rossi, S., Rostalski, T., Rueckert, D., de Arcaute, F.R., Rusnák, M., Sacchi, M., Sahakian, B., Sahuquillo, J., Sakowitz, O., Sala, F., Sanchez-Pena, P., Sanchez-Porras, R., Sandor, J., Santos, E., Sasse, N., Sasu, L., Savo, D., Schipper, I., Schlößer, B., Schmidt, S., Schneider, A., Schoechl, H., Schoonman, G., Schou, R.F., Schwendenwein, E., Schöll, M., Sir, Ö., Skandsen, T., Smakman, L., Smeets, D., Smielewski, P., Sorinola, A., Stamatakis, E., Stanworth, S., Stegemann, K., Steinbüchel, N., Stevens, R., Stewart, W., Steyerberg, E.W., Stocchetti, N., Sundström, N., Synnot, A., Szabó, J., Söderberg, J., Taccone, F.S., Tamás, V., Tanskanen, P., Tascu, A., Taylor, M.S., Te Ao, B., Tenovuo, O., Teodorani, G., Theadom, A., Thomas, M., Tibboel, D., Tolias, C., Tshibanda, J.L., Tudora, C.M., Vajkoczy, P., Valeinis, E., Van Hecke, W., Van Praag, D., Van Roost, D., Van Vlierberghe, E., Vyvere, T.V., Vanhaudenhuyse, A., Vargiolu, A., Vega, E., Verheyden, J., Vespa, P.M., Vik, A., Vilcinis, R., Vizzino, G., Vleggeert-Lankamp, C., Volovici, V., Vulekovic, P., Vámos, Z., Wade, D., Wang, KKW, Wang, L., Wildschut, E., Williams, G., Willumsen, L., Wilson, A., Wilson, L., Winkler, MKL, Ylén, P., Younsi, A., Zaaroor, M., Zhang, Z., Zheng, Z., Zumbo, F., de Lange, S., de Ruiter, GCW, den Boogert, H., van Dijck, J., van Essen, T.A., van Heugten, C., van der Jagt, M., van der Naalt, J., Rocka, Saulius, „Springer' grupė, Apollo - University of Cambridge Repository, Centre of Excellence in Complex Disease Genetics, Department of Mathematics and Statistics, Institute for Molecular Medicine Finland, Biostatistics Helsinki, Aarno Palotie / Principal Investigator, University of Helsinki, Neurokirurgian yksikkö, Clinicum, Samuli Olli Ripatti / Principal Investigator, Complex Disease Genetics, Genomics of Neurological and Neuropsychiatric Disorders, and Statistical and population genetics
Subjects: Neurology, Letter, IMPACT, Critical Care and Intensive Care Medicine, Intracranial hypertension, 0302 clinical medicine, Traumatic brain injury, Clinical Protocols, Trauma Centers, Surveys and Questionnaires, Brain Injuries, Traumatic, Medicine and Health Sciences, Longitudinal Studies, Prospective Studies, Prospective cohort study, Comparative effectiveness research, Survey, Comparative, effectiveness research, Intracranial pressure, Response rate (survey), Medicine (all), Head injury, lcsh:Medical emergencies. Critical care. Intensive care. First aid, 11 Medical And Health Sciences, Orvostudományok, 3. Good health, Europe, Neurosurgery, medicine.medical_specialty, Critical Care, Other Research Radboud Institute for Molecular Life Sciences [Radboudumc 0], PRESSURE, Klinikai orvostudományok, Healthcare improvement science Radboud Institute for Health Sciences [Radboudumc 18], 03 medical and health sciences, ICP, ICU, medicine, MANAGEMENT, Humans, Monitoring, Physiologic, business.industry, MORTALITY, Research, 3112 Neurosciences, HEAD-INJURY, 030208 emergency & critical care medicine, lcsh:RC86-88.9, CARE, medicine.disease, Emergency & Critical Care Medicine, nervous system diseases, traumatic brain injury, comparative effectiveness research, survey, Emergency medicine, Physical therapy, Dentistry (all), MODERATE, Human medicine, business, 030217 neurology & neurosurgery, EUROPEAN-BRAIN
Abstract: Background No definitive evidence exists on how intracranial hypertension should be treated in patients with traumatic brain injury (TBI). It is therefore likely that centers and practitioners individually balance potential benefits and risks of different intracranial pressure (ICP) management strategies, resulting in practice variation. The aim of this study was to examine variation in monitoring and treatment policies for intracranial hypertension in patients with TBI. Methods A 29-item survey on ICP monitoring and treatment was developed on the basis of literature and expert opinion, and it was pilot-tested in 16 centers. The questionnaire was sent to 68 neurotrauma centers participating in the Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Results The survey was completed by 66 centers (97% response rate). Centers were mainly academic hospitals (n = 60, 91%) and designated level I trauma centers (n = 44, 67%). The Brain Trauma Foundation guidelines were used in 49 (74%) centers. Approximately 90% of the participants (n = 58) indicated placing an ICP monitor in patients with severe TBI and computed tomographic abnormalities. There was no consensus on other indications or on peri-insertion precautions. We found wide variation in the use of first- and second-tier treatments for elevated ICP. Approximately half of the centers were classified as using a relatively aggressive approach to ICP monitoring and treatment (n = 32, 48%), whereas the others were considered more conservative (n = 34, 52%). Conclusions Substantial variation was found regarding monitoring and treatment policies in patients with TBI and intracranial hypertension. The results of this survey indicate a lack of consensus between European neurotrauma centers and provide an opportunity and necessity for comparative effectiveness research. Electronic supplementary material The online version of this article (doi:10.1186/s13054-017-1816-9) contains supplementary material, which is available to authorized users.
Published: 2017

29. Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation.

Author: Li Z, Kamnitsas K, Dou Q, Qin C, and Glocker B
Abstract: This paper presents an effective and general data augmentation framework for medical image segmentation. We adopt a computationally efficient and data-efficient gradient-based meta-learning scheme to explicitly align the distribution of training and validation data which is used as a proxy for unseen test data. We improve the current data augmentation strategies with two core designs. First, we learn class-specific training-time data augmentation (TRA) effectively increasing the heterogeneity within the training subsets and tackling the class imbalance common in segmentation. Second, we jointly optimize TRA and test-time data augmentation (TEA), which are closely connected as both aim to align the training and test data distribution but were so far considered separately in previous works. We demonstrate the effectiveness of our method on four medical image segmentation tasks across different scenarios with two state-of-the-art segmentation models, DeepMedic and nnU-Net. Extensive experimentation shows that the proposed data augmentation framework can significantly and consistently improve the segmentation performance when compared to existing solutions. Code is publicly available at https://github.com/ZerojumpLine/JCSAugment.
Published: 2023
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30. Context Label Learning: Improving Background Class Representations in Semantic Segmentation.

Author: Li Z, Kamnitsas K, Ouyang C, Chen C, and Glocker B
Subjects: Image Processing, Computer-Assisted, Semantics, Neural Networks, Computer
Abstract: Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for the segmentation model to learn good decision boundaries with high sensitivity and precision. The issue concerns the highly heterogeneous nature of the background class, resulting in multi-modal distributions. Empirically, we find that neural networks trained with heterogeneous background struggle to map the corresponding contextual samples to compact clusters in feature space. As a result, the distribution over background logit activations may shift across the decision boundary, leading to systematic over-segmentation across different datasets and tasks. In this study, we propose context label learning (CoLab) to improve the context representations by decomposing the background class into several subclasses. Specifically, we train an auxiliary network as a task generator, along with the primary segmentation model, to automatically generate context labels that positively affect the ROI segmentation accuracy. Extensive experiments are conducted on several challenging segmentation tasks and datasets. The results demonstrate that CoLab can guide the segmentation model to map the logits of background samples away from the decision boundary, resulting in significantly improved segmentation accuracy. Code is available at https://github.com/ZerojumpLine/CoLab.
Published: 2023
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31. Author Correction: Federated learning enables big data for rare cancer boundary detection.

Author: Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, and Bakas S
Published: 2023
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32. Federated learning enables big data for rare cancer boundary detection.

Author: Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, and Bakas S
Subjects: Humans, Machine Learning, Rare Diseases, Information Dissemination, Big Data, Glioblastoma
Abstract: Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing., (© 2022. The Author(s).)
Published: 2022
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33. Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: A CENTER-TBI study.

Author: Whitehouse DP, Monteiro M, Czeiter E, Vyvere TV, Valerio F, Ye Z, Amrein K, Kamnitsas K, Xu H, Yang Z, Verheyden J, Das T, Kornaropoulos EN, Steyerberg E, Maas AIR, Wang KKW, Büki A, Glocker B, Menon DK, and Newcombe VFJ
Subjects: Biomarkers, Humans, Male, Prospective Studies, Tomography, X-Ray Computed methods, Brain Injuries, Traumatic diagnosis, Proteomics
Abstract: Background: We aimed to understand the relationship between serum biomarker concentration and lesion type and volume found on computed tomography (CT) following all severities of TBI., Methods: Concentrations of six serum biomarkers (GFAP, NFL, NSE, S100B, t-tau and UCH-L1) were measured in samples obtained <24 hours post-injury from 2869 patients with all severities of TBI, enrolled in the CENTER-TBI prospective cohort study (NCT02210221). Imaging phenotypes were defined as intraparenchymal haemorrhage (IPH), oedema, subdural haematoma (SDH), extradural haematoma (EDH), traumatic subarachnoid haemorrhage (tSAH), diffuse axonal injury (DAI), and intraventricular haemorrhage (IVH). Multivariable polynomial regression was performed to examine the association between biomarker levels and both distinct lesion types and lesion volumes. Hierarchical clustering was used to explore imaging phenotypes; and principal component analysis and k-means clustering of acute biomarker concentrations to explore patterns of biomarker clustering., Findings: 2869 patient were included, 68% (n=1946) male with a median age of 49 years (range 2-96). All severities of TBI (mild, moderate and severe) were included for analysis with majority (n=1946, 68%) having a mild injury (GCS 13-15). Patients with severe diffuse injury (Marshall III/IV) showed significantly higher levels of all measured biomarkers, with the exception of NFL, than patients with focal mass lesions (Marshall grades V/VI). Patients with either DAI+IVH or SDH+IPH+tSAH, had significantly higher biomarker concentrations than patients with EDH. Higher biomarker concentrations were associated with greater volume of IPH (GFAP, S100B, t-tau;adj r2 range:0·48-0·49; p<0·05), oedema (GFAP, NFL, NSE, t-tau, UCH-L1;adj r2 range:0·44-0·44; p<0·01), IVH (S100B;adj r2 range:0.48-0.49; p<0.05), Unsupervised k-means biomarker clustering revealed two clusters explaining 83·9% of variance, with phenotyping characteristics related to clinical injury severity., Interpretation: Interpretation: Biomarker concentration within 24 hours of TBI is primarily related to severity of injury and intracranial disease burden, rather than pathoanatomical type of injury., Funding: CENTER-TBI is funded by the European Union 7th Framework programme (EC grant 602150)., Competing Interests: Declaration of interests DKM reports: grants from the European Union (EU), the National Institute for Health Research UK supporting the submitted work; grants from GlaxoSmithKline Ltd and Lantmannen AB, consulting fees from Calico LLC, GlaxoSmithKline Ltd, Lantmannen AB and NeuroTrauma Sciences LLC, and personal fees from Integra Neurosciences outside the submitted work. BG has received grants from European Commission and UK Research and Innovation Engineering and Physical Sciences Research Council, during the conduct of this study; and is Scientific Advisor for Kheiron Medical Technologies, Advisor and Scientific Lead of the HeartFlow-Imperial Research Team, outside the submitted work. MM reports a ERC grant agreement and consultancy fees from Triradiate Industries, outside the submitted work. ES reports a FP7 grant from the EU supporting the submitted work, and royalties for the book “Clinical Prediction Models” published by Springer. AIRM reports a FP7 grant from the EU supporting the submitted work, and grants from NeuroTrauma Sciences, Hannelore Kohl Foundation, and IntegraLife Sciences, personal fees from PresSura Neuro as DSMB chairman outside of the submitted work. KKWW reports a FP7 grant from the EU supporting the submitted work, and as a shareholder of Gryphon Bio, Inc. VFJN reports an Academy of Medical Sciences/The Health Foundation Clinician Scientist Fellowship, during the conduct of this study; a grant from Roche Pharmaceuticals, and honorarium for talks from Neurodiem, outside the submitted work. All other authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)
Published: 2022
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34. Analyzing Overfitting Under Class Imbalance in Neural Networks for Image Segmentation.

Author: Li Z, Kamnitsas K, and Glocker B
Subjects: Databases, Factual, Image Processing, Computer-Assisted, Neural Networks, Computer
Abstract: Class imbalance poses a challenge for developing unbiased, accurate predictive models. In particular, in image segmentation neural networks may overfit to the foreground samples from small structures, which are often heavily under-represented in the training set, leading to poor generalization. In this study, we provide new insights on the problem of overfitting under class imbalance by inspecting the network behavior. We find empirically that when training with limited data and strong class imbalance, at test time the distribution of logit activations may shift across the decision boundary, while samples of the well-represented class seem unaffected. This bias leads to a systematic under-segmentation of small structures. This phenomenon is consistently observed for different databases, tasks and network architectures. To tackle this problem, we introduce new asymmetric variants of popular loss functions and regularization techniques including a large margin loss, focal loss, adversarial training, mixup and data augmentation, which are explicitly designed to counter logit shift of the under-represented classes. Extensive experiments are conducted on several challenging segmentation tasks. Our results demonstrate that the proposed modifications to the objective function can lead to significantly improved segmentation accuracy compared to baselines and alternative approaches.
Published: 2021
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35. Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data.

Author: Mitchell JR, Kamnitsas K, Singleton KW, Whitmire SA, Clark-Swanson KR, Ranjbar S, Rickertsen CR, Johnston SK, Egan KM, Rollison DE, Arrington J, Krecke KN, Passe TJ, Verdoorn JT, Nagelschneider AA, Carr CM, Port JD, Patton A, Campeau NG, Liebo GB, Eckel LJ, Wood CP, Hunt CH, Vibhute P, Nelson KD, Hoxworth JM, Patel AC, Chong BW, Ross JS, Boxerman JL, Vogelbaum MA, Hu LS, Glocker B, and Swanson KR
Abstract: Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations. Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap). Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively ( p < 0.00007 ). The DL method achieved a mean Dice coefficient of 0.87 on the test cases. Conclusions: This was the first objective comparison of automated and human segmentation using a blinded controlled assessment study. Our DL system learned to outperform its "human teachers" and produced output that was better, on average, than its training data., (© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.)
Published: 2020
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36. Relationship between Measures of Cerebrovascular Reactivity and Intracranial Lesion Progression in Acute Traumatic Brain Injury Patients: A CENTER-TBI Study.

Author: Mathieu F, Zeiler FA, Ercole A, Monteiro M, Kamnitsas K, Glocker B, Whitehouse DP, Das T, Smielewski P, Czosnyka M, Hutchinson PJ, Newcombe VFJ, and Menon DK
Subjects: Adult, Brain Injuries diagnostic imaging, Brain Injuries epidemiology, Brain Injuries physiopathology, Brain Injuries, Traumatic epidemiology, Cohort Studies, Europe epidemiology, Female, Humans, Male, Middle Aged, Tomography, X-Ray Computed methods, Tomography, X-Ray Computed standards, Treatment Outcome, Brain Injuries, Traumatic diagnostic imaging, Brain Injuries, Traumatic physiopathology, Cerebrovascular Circulation physiology, Disease Progression, Intensive Care Units standards, Intersectoral Collaboration
Abstract: Failure of cerebral autoregulation has been linked to unfavorable outcome after traumatic brain injury (TBI). Preliminary evidence from a small, retrospective, single-center analysis suggests that autoregulatory dysfunction may be associated with traumatic lesion expansion, particularly for pericontusional edema. The goal of this study was to further explore these associations using prospective, multi-center data from the Collaborative European Neurotrauma Effectiveness Research in TBI (CENTER-TBI) and to further explore the relationship between autoregulatory failure, lesion progression, and patient outcome. A total of 88 subjects from the CENTER-TBI High Resolution ICU Sub-Study cohort were included. All patients had an admission computed tomography (CT) scan and early repeat scan available, as well as high-frequency neurophysiological recordings covering the between-scan interval. Using a novel, semiautomated approach at lesion segmentation, we calculated absolute changes in volume of contusion core, pericontusional edema, and extra-axial hemorrhage between the imaging studies. We then evaluated associations between cerebrovascular reactivity metrics and radiological lesion progression using mixed-model regression. Analyses were adjusted for baseline covariates and non-neurophysiological factors associated with lesion growth using multi-variate methods. Impairment in cerebrovascular reactivity was significantly associated with progression of pericontusional edema and, to a lesser degree, intraparenchymal hemorrhage. In contrast, there were no significant associations with extra-axial hemorrhage. The strongest relationships were observed between RAC-based metrics and edema formation. Pulse amplitude index showed weaker, but consistent, associations with contusion growth. Cerebrovascular reactivity metrics remained strongly associated with lesion progression after taking into account contributions from non-neurophysiological factors and mean cerebral perfusion pressure. Total hemorrhagic core and edema volumes on repeat CT were significantly larger in patients who were deceased at 6 months, and the amount of edema was greater in patients with an unfavourable outcome (Glasgow Outcome Scale-Extended 1-4). Our study suggests associations between autoregulatory failure, traumatic edema progression, and poor outcome. This is in keeping with findings from a single-center retrospective analysis, providing multi-center prospective data to support those results.
Published: 2020
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37. Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study.

Author: Monteiro M, Newcombe VFJ, Mathieu F, Adatia K, Kamnitsas K, Ferrante E, Das T, Whitehouse D, Rueckert D, Menon DK, and Glocker B
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Brain diagnostic imaging, Child, Europe, Female, Humans, Male, Middle Aged, Reproducibility of Results, Semantics, Young Adult, Brain Injuries, Traumatic diagnostic imaging, Deep Learning, Radiographic Image Interpretation, Computer-Assisted methods, Tomography, X-Ray Computed methods
Abstract: Background: CT is the most common imaging modality in traumatic brain injury (TBI). However, its conventional use requires expert clinical interpretation and does not provide detailed quantitative outputs, which may have prognostic importance. We aimed to use deep learning to reliably and efficiently quantify and detect different lesion types., Methods: Patients were recruited between Dec 9, 2014, and Dec 17, 2017, in 60 centres across Europe. We trained and validated an initial convolutional neural network (CNN) on expert manual segmentations (dataset 1). This CNN was used to automatically segment a new dataset of scans, which we then corrected manually (dataset 2). From this dataset, we used a subset of scans to train a final CNN for multiclass, voxel-wise segmentation of lesion types. The performance of this CNN was evaluated on a test subset. Performance was measured for lesion volume quantification, lesion progression, and lesion detection and lesion volume classification. For lesion detection, external validation was done on an independent set of 500 patients from India., Findings: 98 scans from one centre were included in dataset 1. Dataset 2 comprised 839 scans from 38 centres: 184 scans were used in the training subset and 655 in the test subset. Compared with manual reference, CNN-derived lesion volumes showed a mean difference of 0·86 mL (95% CI -5·23 to 6·94) for intraparenchymal haemorrhage, 1·83 mL (-12·01 to 15·66) for extra-axial haemorrhage, 2·09 mL (-9·38 to 13·56) for perilesional oedema, and 0·07 mL (-1·00 to 1·13) for intraventricular haemorrhage., Interpretation: We show the ability of a CNN to separately segment, quantify, and detect multiclass haemorrhagic lesions and perilesional oedema. These volumetric lesion estimates allow clinically relevant quantification of lesion burden and progression, with potential applications for personalised treatment strategies and clinical research in TBI., Funding: European Union 7th Framework Programme, Hannelore Kohl Stiftung, OneMind, NeuroTrauma Sciences, Integra Neurosciences, European Research Council Horizon 2020., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
Published: 2020
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38. Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models.

Author: Biffi C, Cerrolaza JJ, Tarroni G, Bai W, de Marvao A, Oktay O, Ledig C, Le Folgoc L, Kamnitsas K, Doumou G, Duan J, Prasad SK, Cook SA, O'Regan DP, and Rueckert D
Subjects: Hippocampus, Humans, Alzheimer Disease diagnostic imaging, Magnetic Resonance Imaging
Abstract: Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging.
Published: 2020
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39. Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data.

Author: Wu O, Winzeck S, Giese AK, Hancock BL, Etherton MR, Bouts MJRJ, Donahue K, Schirmer MD, Irie RE, Mocking SJT, McIntosh EC, Bezerra R, Kamnitsas K, Frid P, Wasselius J, Cole JW, Xu H, Holmegaard L, Jiménez-Conde J, Lemmens R, Lorentzen E, McArdle PF, Meschia JF, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Stanne TM, Thijs V, Vagal A, Woo D, Bevan S, Kittner SJ, Mitchell BD, Rosand J, Worrall BB, Jern C, Lindgren AG, Maguire J, and Rost NS
Subjects: Adult, Aged, Aged, 80 and over, Algorithms, Big Data, Brain Ischemia epidemiology, Female, Humans, Image Processing, Computer-Assisted, Machine Learning, Male, Middle Aged, Neural Networks, Computer, Observer Variation, Phenotype, Retrospective Studies, Risk Factors, Socioeconomic Factors, Stroke epidemiology, Brain Ischemia diagnostic imaging, Diffusion Magnetic Resonance Imaging methods, Stroke diagnostic imaging
Abstract: Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm 3 (0.9-16.6 cm 3 ). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.
Published: 2019
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40. Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI.

Author: Winzeck S, Mocking SJT, Bezerra R, Bouts MJRJ, McIntosh EC, Diwan I, Garg P, Chutinet A, Kimberly WT, Copen WA, Schaefer PW, Ay H, Singhal AB, Kamnitsas K, Glocker B, Sorensen AG, and Wu O
Subjects: Aged, Diffusion Magnetic Resonance Imaging methods, Female, Humans, Male, Middle Aged, Stroke diagnostic imaging, Brain Ischemia diagnostic imaging, Image Interpretation, Computer-Assisted methods, Neural Networks, Computer, Neuroimaging methods
Abstract: Background and Purpose: Accurate automated infarct segmentation is needed for acute ischemic stroke studies relying on infarct volumes as an imaging phenotype or biomarker that require large numbers of subjects. This study investigated whether an ensemble of convolutional neural networks trained on multiparametric DWI maps outperforms single networks trained on solo DWI parametric maps., Materials and Methods: Convolutional neural networks were trained on combinations of DWI, ADC, and low b-value-weighted images from 116 subjects. The performances of the networks (measured by the Dice score, sensitivity, and precision) were compared with one another and with ensembles of 5 networks. To assess the generalizability of the approach, we applied the best-performing model to an independent Evaluation Cohort of 151 subjects. Agreement between manual and automated segmentations for identifying patients with large lesion volumes was calculated across multiple thresholds (21, 31, 51, and 70 cm 3 )., Results: An ensemble of convolutional neural networks trained on DWI, ADC, and low b-value-weighted images produced the most accurate acute infarct segmentation over individual networks ( P < .001). Automated volumes correlated with manually measured volumes (Spearman ρ = 0.91, P < .001) for the independent cohort. For the task of identifying patients with large lesion volumes, agreement between manual outlines and automated outlines was high (Cohen κ, 0.86-0.90; P < .001)., Conclusions: Acute infarcts are more accurately segmented using ensembles of convolutional neural networks trained with multiparametric maps than by using a single model trained with a solo map. Automated lesion segmentation has high agreement with manual techniques for identifying patients with large lesion volumes., (© 2019 by American Journal of Neuroradiology.)
Published: 2019
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41. Evaluating reinforcement learning agents for anatomical landmark detection.

Author: Alansary A, Oktay O, Li Y, Folgoc LL, Hou B, Vaillant G, Kamnitsas K, Vlontzos A, Glocker B, Kainz B, and Rueckert D
Subjects: Adult, Female, Head embryology, Humans, Pregnancy, Anatomic Landmarks, Brain diagnostic imaging, Deep Learning, Head diagnostic imaging, Heart diagnostic imaging, Imaging, Three-Dimensional methods, Magnetic Resonance Imaging methods
Abstract: Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans. An artificial RL agent learns to identify the optimal path to the landmark by interacting with an environment, in our case 3D images. Furthermore, we investigate the use of fixed- and multi-scale search strategies with novel hierarchical action steps in a coarse-to-fine manner. Several deep Q-network (DQN) architectures are evaluated for detecting multiple landmarks using three different medical imaging datasets: fetal head ultrasound (US), adult brain and cardiac magnetic resonance imaging (MRI). The performance of our agents surpasses state-of-the-art supervised and RL methods. Our experiments also show that multi-scale search strategies perform significantly better than fixed-scale agents in images with large field of view and noisy background such as in cardiac MRI. Moreover, the novel hierarchical steps can significantly speed up the searching process by a factor of 4-5 times., (Copyright © 2019 Elsevier B.V. All rights reserved.)
Published: 2019
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42. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation.

Author: Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook SA, de Marvao A, Dawes T, O'Regan DP, Kainz B, Glocker B, and Rueckert D
Subjects: Algorithms, Cardiomyopathies diagnostic imaging, Databases, Factual, Heart diagnostic imaging, Humans, Magnetic Resonance Imaging, Cardiac Imaging Techniques methods, Imaging, Three-Dimensional methods, Neural Networks, Computer
Abstract: Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning-based techniques. However, in most recent and promising techniques such as CNN-based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac data sets and public benchmarks. In addition, we demonstrate how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.
Published: 2018
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43. Regional brain morphometry in patients with traumatic brain injury based on acute- and chronic-phase magnetic resonance imaging.

Author: Ledig C, Kamnitsas K, Koikkalainen J, Posti JP, Takala RSK, Katila A, Frantzén J, Ala-Seppälä H, Kyllönen A, Maanpää HR, Tallus J, Lötjönen J, Glocker B, Tenovuo O, and Rueckert D
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Cross-Sectional Studies, Female, Humans, Longitudinal Studies, Male, Middle Aged, Young Adult, Brain Injuries, Traumatic diagnostic imaging, Brain Injuries, Traumatic pathology, Magnetic Resonance Imaging methods
Abstract: Traumatic brain injury (TBI) is caused by a sudden external force and can be very heterogeneous in its manifestation. In this work, we analyse T1-weighted magnetic resonance (MR) brain images that were prospectively acquired from patients who sustained mild to severe TBI. We investigate the potential of a recently proposed automatic segmentation method to support the outcome prediction of TBI. Specifically, we extract meaningful cross-sectional and longitudinal measurements from acute- and chronic-phase MR images. We calculate regional volume and asymmetry features at the acute/subacute stage of the injury (median: 19 days after injury), to predict the disability outcome of 67 patients at the chronic disease stage (median: 229 days after injury). Our results indicate that small structural volumes in the acute stage (e.g. of the hippocampus, accumbens, amygdala) can be strong predictors for unfavourable disease outcome. Further, group differences in atrophy are investigated. We find that patients with unfavourable outcome show increased atrophy. Among patients with severe disability outcome we observed a significantly higher mean reduction of cerebral white matter (3.1%) as compared to patients with low disability outcome (0.7%).
Published: 2017
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44. SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound.

Author: Baumgartner CF, Kamnitsas K, Matthew J, Fletcher TP, Smith S, Koch LM, Kainz B, and Rueckert D
Subjects: Algorithms, Female, Humans, Pregnancy, Video Recording, Image Processing, Computer-Assisted methods, Neural Networks, Computer, Ultrasonography, Prenatal methods
Abstract: Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box. An important contribution is that the network learns to localize the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localization task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localization on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modeling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localization task.
Published: 2017
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45. Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach.

Author: Lavdas I, Glocker B, Kamnitsas K, Rueckert D, Mair H, Sandhu A, Taylor SA, Aboagye EO, and Rockall AG
Subjects: Adult, Aged, Automation, Female, Humans, Male, Middle Aged, Young Adult, Image Processing, Computer-Assisted methods, Magnetic Resonance Imaging, Neural Networks, Computer, Whole Body Imaging
Abstract: Purpose: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers., Methods: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross-validation experiments were run on 34 artifact-free subjects. We report three overlap and three surface distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average surface distance (ASD), root-mean-square surface distance (RMSSD), and Hausdorff distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training., Results: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 ± 0.18, RE = 0.73 ± 0.18, PR = 0.71 ± 0.14, CNNs: DSC = 0.81 ± 0.13, RE = 0.83 ± 0.14, PR = 0.82 ± 0.10, MA: DSC = 0.71 ± 0.22, RE = 0.70 ± 0.34, PR = 0.77 ± 0.15. Mean surface distance metrics for all the segmented structures were: CFs: ASD = 13.5 ± 11.3 mm, RMSSD = 34.6 ± 37.6 mm and HD = 185.7 ± 194.0 mm, CNNs; ASD = 5.48 ± 4.84 mm, RMSSD = 17.0 ± 13.3 mm and HD = 199.0 ± 101.2 mm, MA: ASD = 4.22 ± 2.42 mm, RMSSD = 6.13 ± 2.55 mm, and HD = 38.9 ± 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder., Conclusions: Three state-of-the-art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance., (© 2017 American Association of Physicists in Medicine.)
Published: 2017
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46. Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth.

Author: Valindria VV, Lavdas I, Bai W, Kamnitsas K, Aboagye EO, Rockall AG, Rueckert D, and Glocker B
Subjects: Algorithms, Reproducibility of Results
Abstract: When integrating computational tools, such as automatic segmentation, into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data and, in particular, to detect when an automatic method fails. However, this is difficult to achieve due to the absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross validation, because validation data are often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared with a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA, we take the predicted segmentation from a new image to train a reverse classifier, which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as a part of large-scale image analysis studies.
Published: 2017
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47. Multi-channel MRI segmentation of eye structures and tumors using patient-specific features.

Author: Ciller C, De Zanet S, Kamnitsas K, Maeder P, Glocker B, Munier FL, Rueckert D, Thiran JP, Bach Cuadra M, and Sznitman R
Subjects: Algorithms, Cornea anatomy & histology, Cornea diagnostic imaging, Eye anatomy & histology, Eye Neoplasms pathology, Humans, Lens, Crystalline diagnostic imaging, Models, Anatomic, Sclera anatomy & histology, Sclera diagnostic imaging, Vitreous Body anatomy & histology, Vitreous Body diagnostic imaging, Eye diagnostic imaging, Eye Neoplasms diagnostic imaging, Imaging, Three-Dimensional methods, Magnetic Resonance Imaging methods
Abstract: Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed.
Published: 2017
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48. DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks.

Author: Rajchl M, Lee MC, Oktay O, Kamnitsas K, Passerat-Palmbach J, Bai W, Damodaram M, Rutherford MA, Hajnal JV, Kainz B, and Rueckert D
Subjects: Algorithms, Brain, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Machine Learning, Magnetic Resonance Imaging, Monte Carlo Method, Neural Networks, Computer
Abstract: In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. It extends the approach of the well-known GrabCut [1] method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naïve approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.
Published: 2017
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49. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation.

Author: Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, Rueckert D, and Glocker B
Subjects: Brain Injuries, Traumatic pathology, Brain Ischemia pathology, Brain Neoplasms pathology, Humans, Reproducibility of Results, Sensitivity and Specificity, Brain diagnostic imaging, Brain pathology, Brain Injuries, Traumatic diagnostic imaging, Brain Ischemia diagnostic imaging, Brain Neoplasms diagnostic imaging, Neural Networks, Computer
Abstract: We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available., (Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.)
Published: 2017
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50. ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI.

Author: Maier O, Menze BH, von der Gablentz J, Ḧani L, Heinrich MP, Liebrand M, Winzeck S, Basit A, Bentley P, Chen L, Christiaens D, Dutil F, Egger K, Feng C, Glocker B, Götz M, Haeck T, Halme HL, Havaei M, Iftekharuddin KM, Jodoin PM, Kamnitsas K, Kellner E, Korvenoja A, Larochelle H, Ledig C, Lee JH, Maes F, Mahmood Q, Maier-Hein KH, McKinley R, Muschelli J, Pal C, Pei L, Rangarajan JR, Reza SMS, Robben D, Rueckert D, Salli E, Suetens P, Wang CW, Wilms M, Kirschke JS, Kr Amer UM, Münte TF, Schramm P, Wiest R, Handels H, and Reyes M
Subjects: Humans, Algorithms, Benchmarking, Image Interpretation, Computer-Assisted methods, Magnetic Resonance Imaging methods, Stroke diagnostic imaging
Abstract: Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org)., (Copyright © 2016 Elsevier B.V. All rights reserved.)
Published: 2017
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