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1. Metrics reloaded: recommendations for image analysis validation

Author

Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M.D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., Reyes, M., Riegler, M.A., Wiesenfarth, M., Kavur, A.E., Sudre, C.H., Baumgartner, M., Eisenmann, M., Heckmann-Notzel, D., Radsch, T., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Blaschko, M.B., Cardoso, M.J., Cheplygina, V., Cimini, B.A., Collins, G.S., Farahani, K., Ferrer, L., Galdran, A., Ginneken, B. van, Haase, R., Hashimoto, D.A., Hoffman, M.M., Huisman, M., Jannin, P., Kahn, C.E., Kainmueller, D., Kainz, B., Karargyris, A., Karthikesalingam, A., Kofler, F., Kopp-Schneider, A., Kreshuk, A., Kurc, T., Landman, B.A., Litjens, G.J., Madani, A., Maier-Hein, K., Martel, A.L., Mattson, P., Meijering, E., Menze, B., Moons, K.G., Muller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rajpoot, N., Rieke, N., Saez-Rodriguez, J., Sanchez, C.I., Shetty, S., Smeden, M. van, Summers, R.M., Taha, A.A., Tiulpin, A., Tsaftaris, S.A., Calster, B. van, Varoquaux, G., Jager, P.F., Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M.D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., Reyes, M., Riegler, M.A., Wiesenfarth, M., Kavur, A.E., Sudre, C.H., Baumgartner, M., Eisenmann, M., Heckmann-Notzel, D., Radsch, T., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Blaschko, M.B., Cardoso, M.J., Cheplygina, V., Cimini, B.A., Collins, G.S., Farahani, K., Ferrer, L., Galdran, A., Ginneken, B. van, Haase, R., Hashimoto, D.A., Hoffman, M.M., Huisman, M., Jannin, P., Kahn, C.E., Kainmueller, D., Kainz, B., Karargyris, A., Karthikesalingam, A., Kofler, F., Kopp-Schneider, A., Kreshuk, A., Kurc, T., Landman, B.A., Litjens, G.J., Madani, A., Maier-Hein, K., Martel, A.L., Mattson, P., Meijering, E., Menze, B., Moons, K.G., Muller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rajpoot, N., Rieke, N., Saez-Rodriguez, J., Sanchez, C.I., Shetty, S., Smeden, M. van, Summers, R.M., Taha, A.A., Tiulpin, A., Tsaftaris, S.A., Calster, B. van, Varoquaux, G., and Jager, P.F.

Abstract

Contains fulltext : 305368.pdf (Publisher’s version ) (Closed access), Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

Published
2024

2. Understanding metric-related pitfalls in image analysis validation

Author

Reinke, A., Tizabi, M.D., Baumgartner, M., Eisenmann, M., Heckmann-Notzel, D., Kavur, A.E., Radsch, T., Sudre, C.H., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Buettner, F., Cardoso, M.J., Cheplygina, V., Chen, J., Christodoulou, E., Cimini, B.A., Farahani, K., Ferrer, L., Galdran, A., Ginneken, B. van, Glocker, B., Godau, P., Hashimoto, D.A., Hoffman, M.M., Huisman, M., Isensee, F., Jannin, P., Kahn, C.E., Kainmueller, D., Kainz, B., Karargyris, A., Kleesiek, J., Kofler, F., Kooi, T., Kopp-Schneider, A., Kozubek, M., Kreshuk, A., Kurc, T., Landman, B.A., Litjens, G.J., Madani, A., Maier-Hein, K., Martel, A.L., Meijering, E., Menze, B., Moons, K.G., Muller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rafelski, S.M., Rajpoot, N., Reyes, M., Riegler, M.A., Rieke, N., Saez-Rodriguez, J., Sanchez, C.I., Shetty, S., Summers, R.M., Taha, A.A., Tiulpin, A., Tsaftaris, S.A., Calster, B. van, Varoquaux, G., Yaniv, Z.R., Jager, P.F., Maier-Hein, L., Reinke, A., Tizabi, M.D., Baumgartner, M., Eisenmann, M., Heckmann-Notzel, D., Kavur, A.E., Radsch, T., Sudre, C.H., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Buettner, F., Cardoso, M.J., Cheplygina, V., Chen, J., Christodoulou, E., Cimini, B.A., Farahani, K., Ferrer, L., Galdran, A., Ginneken, B. van, Glocker, B., Godau, P., Hashimoto, D.A., Hoffman, M.M., Huisman, M., Isensee, F., Jannin, P., Kahn, C.E., Kainmueller, D., Kainz, B., Karargyris, A., Kleesiek, J., Kofler, F., Kooi, T., Kopp-Schneider, A., Kozubek, M., Kreshuk, A., Kurc, T., Landman, B.A., Litjens, G.J., Madani, A., Maier-Hein, K., Martel, A.L., Meijering, E., Menze, B., Moons, K.G., Muller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rafelski, S.M., Rajpoot, N., Reyes, M., Riegler, M.A., Rieke, N., Saez-Rodriguez, J., Sanchez, C.I., Shetty, S., Summers, R.M., Taha, A.A., Tiulpin, A., Tsaftaris, S.A., Calster, B. van, Varoquaux, G., Yaniv, Z.R., Jager, P.F., and Maier-Hein, L.

Abstract

Contains fulltext : 305370.pdf (Publisher’s version ) (Closed access), Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Published
2024

3. The Meta VCI Map consortium for meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping: Design and multicenter pilot study

Author

DeCarli, C., Fletcher, E.A., Maillard, P., Barnes, J., Sudre, C.H., Schott, J.M., Ikram, M.A., Papma, J.M., Steketee, R.M.E., Vernooij, M.W., Bordet, R., Lopes, R., Huang, C.-W., Frayne, R., McCreary, C.R., Smith, E.E., Backes, W., Köhler, S., van Oostenbrugge, R.J., Staals, J., Verhey, F., Cheng, C.Y., Kalaria, R.N., Werring, D., Hsu, J.L., Huang, K.-L., van der Grond, J., Jukema, J.W., van der Mast, R.C., Nijboer, T.C.W., Yu, K.-H., Schmidt, R., Pirpamer, L., MacIntosh, B.J., Robertson, A.D., de Leeuw, F.-E., Tuladhar, A.M., Chaturvedi, N., Tillin, T., Brodaty, H., Sachdev, P., Barkhof, F., van der Flier, W.M., Kappelle, L.J., Weaver, Nick A., Zhao, Lei, Biesbroek, J. Matthijs, Kuijf, Hugo J., Aben, Hugo P., Bae, Hee-Joon, Caballero, Miguel Á.A., Chappell, Francesca M., Chen, Christopher P.L.H., Dichgans, Martin, Duering, Marco, Georgakis, Marios K., van der Giessen, Ruben S., Gyanwali, Bibek, Hamilton, Olivia K.L., Hilal, Saima, vom Hofe, Elise M., de Kort, Paul L.M., Koudstaal, Peter J., Lam, Bonnie Y.K., Lim, Jae-Sung, Makin, Stephen D.J., Mok, Vincent C.T., Shi, Lin, Valdés Hernández, Maria C., Venketasubramanian, Narayanaswamy, Wardlaw, Joanna M., Wollenweber, Frank A., Wong, Adrian, Xin, Xu, and Biessels, Geert Jan

Published
2019
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4. Optimal symptom combinations to aid COVID-19 case identification: Analysis from a community-based, prospective, observational cohort

Author

Antonelli, M., primary, Capdevila, J., additional, Chaudhari, A., additional, Granerod, J., additional, Canas, L.S., additional, Graham, M.S., additional, Klaser, K., additional, Modat, M., additional, Molteni, E., additional, Murray, B., additional, Sudre, C.H., additional, Davies, R., additional, May, A., additional, Nguyen, L.H., additional, Drew, D.A., additional, Joshi, A., additional, Chan, A.T., additional, Cramer, J.P., additional, Spector, T., additional, Wolf, J., additional, Ourselin, S., additional, Steves, C.J., additional, and Loeliger, A.E., additional

Published
2021
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5. Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Author

Sudre, C.H. Panovska-Griffiths, J. Sanverdi, E. Brandner, S. Katsaros, V.K. Stranjalis, G. Pizzini, F.B. Ghimenton, C. Surlan-Popovic, K. Avsenik, J. Spampinato, M.V. Nigro, M. Chatterjee, A.R. Attye, A. Grand, S. Krainik, A. Anzalone, N. Conte, G.M. Romeo, V. Ugga, L. Elefante, A. Ciceri, E.F. Guadagno, E. Kapsalaki, E. Roettger, D. Gonzalez, J. Boutelier, T. Cardoso, M.J. Bisdas, S.

Abstract

Background: Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status. Methods: Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon two-sample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features. Results: Shape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1). Conclusions: Despite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading. © 2020 The Author(s).

Published
2020

6. Investigating the clinico-anatomical dissociation in the behavioral variant of Alzheimer disease

Author

Singleton, E.H., Pijnenburg, YA, Sudre, C.H., de Groot, C., Kochova, E., Barkhof, F. (Frederik), La Joie, R., Rosen, H.J., Seeley, WW, Miller, B., Cardoso, MJ, Papma, J., Scheltens, P. (Philip), Rabinovici, G.D., Ossenkoppele, R. (Rik), Singleton, E.H., Pijnenburg, YA, Sudre, C.H., de Groot, C., Kochova, E., Barkhof, F. (Frederik), La Joie, R., Rosen, H.J., Seeley, WW, Miller, B., Cardoso, MJ, Papma, J., Scheltens, P. (Philip), Rabinovici, G.D., and Ossenkoppele, R. (Rik)

Abstract

Background: We previously found temporoparietal-predominant atrophy patterns in the behavioral variant of Alzheimer’s disease (bvAD), with relative sparing of frontal regions. Here, we aimed to understand the clinicoanatomical dissociation in bvAD based on alternative neuroimaging markers. Methods: We retrospectively included 150 participants, including 29 bvAD, 28 “typical” amnestic-predominant AD (tAD), 28 behavioral variant of frontotemporal dementia (bvFTD), and 65 cognitively normal participants. Patients with bvAD were compared with other diagnostic groups on glucose metabolism and metabolic connectivity measured by [18F]FDG-PET, and on subcortical gray matter and white matt

Published
2020
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7. The Meta VCI Map consortium for meta‐analyses on strategic lesion locations for vascular cognitive impairment using lesion‐symptom mapping: design and multicenter pilot study

Author

Weaver, Nick A., Zhao, Lei, Biesbroek, J. Matthijs, Kuijf, Hugo J., Aben, Hugo P., Bae, Hee‐Joon, Caballero, Miguel Á.A., Chappell, Francesca M., Chen, Christopher P.L.H., Dichgans, Martin, Duering, Marco, Georgakis, Marios K., Giessen, Ruben S., Gyanwali, Bibek, Hamilton, Olivia K.L., Hilal, Saima, Hofe, Elise M., Kort, Paul L.M., Koudstaal, Peter J., Lam, Bonnie Y.K., Lim, Jae‐Sung, Makin, Stephen D.J., Mok, Vincent C.T., Shi, Lin, Valdés Hernández, Maria C., Venketasubramanian, Narayanaswamy, Wardlaw, Joanna M., Wollenweber, Frank A., Wong, Adrian, Xin, Xu, DeCarli, C., Fletcher, E.A., Maillard, P., Barnes, J., Sudre, C.H., Schott, J.M., Ikram, M.A., Papma, J.M., Steketee, R.M.E., Vernooij, M.W., Bordet, R., Lopes, R., Huang, C.‐W., Frayne, R., McCreary, C.R., Smith, E.E., Backes, W., Köhler, S., Oostenbrugge, R.J., Staals, J., Verhey, F., Cheng, C.Y., Kalaria, R.N., Werring, D., Hsu, J.L., Huang, K.‐L., Grond, J., Jukema, J.W., Mast, R.C., Nijboer, T.C.W., Yu, K.‐H., Schmidt, R., Pirpamer, L., MacIntosh, B.J., Robertson, A.D., Leeuw, F.‐E., Tuladhar, A.M., Chaturvedi, N., Tillin, T., Brodaty, H., Sachdev, P., Barkhof, F., Flier, W.M., Kappelle, L.J., and Biessels, Geert Jan

Abstract

Introduction: \ud The Meta VCI Map consortium performs meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping. Integration of data from different cohorts will increase sample sizes, to improve brain lesion coverage and support comprehensive lesion-symptom mapping studies.\ud \ud Methods: \ud Cohorts with available imaging on white matter hyperintensities or infarcts and cognitive testing were invited. We performed a pilot study to test the feasibility of multicenter data processing and analysis and determine the benefits to lesion coverage.\ud \ud Results: \ud Forty-seven groups have joined Meta VCI Map (stroke n = 7800 patients; memory clinic n = 4900; population-based n = 14,400). The pilot study (six ischemic stroke cohorts, n = 878) demonstrated feasibility of multicenter data integration (computed tomography/magnetic resonance imaging) and achieved marked improvement of lesion coverage.\ud \ud Discussion: \ud Meta VCI Map will provide new insights into the relevance of vascular lesion location for cognitive dysfunction. After the successful pilot study, further projects are being prepared. Other investigators are welcome to join.

Published
2019

9. White matter hyperintensities in progranulin-associated frontotemporal dementia: A longitudinal GENFI study

Author

Sudre, C.H. (Carole H.), Bocchetta, M. (Martina), Heller, C. (Carolin), Convery, R. (Rhian), Neason, M. (Mollie), Moore, K.M. (Katrina M.), Cash, D.M. (David M.), Thomas, D.L. (David L), Woollacott, I.O.C. (Ione O.C.), Foiani, M. (Martha), Heslegrave, A. (Amanda), Shafei, R. (Rachelle), Greaves, C. (Caroline), Swieten, J.C. (John) van, Moreno, F. (Fermin), Sánchez-Valle, R. (Raquel), Borroni, B. (Barbara), Laforce, R. (Robert), Masellis, M. (Mario), Tartaglia, M.C. (Maria Carmela), Graff, C. (Caroline), Galimberti, D. (Daniela), Rowe, J.B. (James), Finger, E. (Elizabeth), Synofzik, M. (Matthis), Vandenberghe, R. (Rik), De Mendonça, A. (Alexandre), Tagliavini, F. (Fabrizio), Santana, I. (Isabel), Ducharme, S. (Simon), Butler, C. (Chris), Gerhard, A. (Alex), Levin, J. (Johannes), Danek, A. (Adrian), Frisoni, G.B. (Giovanni B.), Sorbi, S. (Sandro), Otto, M. (Markus), Zetterberg, H. (Henrik), Ourselin, S. (Sebastien), Cardoso, M.J. (M. Jorge), Rohrer, J.D. (Jonathan), Sudre, C.H. (Carole H.), Bocchetta, M. (Martina), Heller, C. (Carolin), Convery, R. (Rhian), Neason, M. (Mollie), Moore, K.M. (Katrina M.), Cash, D.M. (David M.), Thomas, D.L. (David L), Woollacott, I.O.C. (Ione O.C.), Foiani, M. (Martha), Heslegrave, A. (Amanda), Shafei, R. (Rachelle), Greaves, C. (Caroline), Swieten, J.C. (John) van, Moreno, F. (Fermin), Sánchez-Valle, R. (Raquel), Borroni, B. (Barbara), Laforce, R. (Robert), Masellis, M. (Mario), Tartaglia, M.C. (Maria Carmela), Graff, C. (Caroline), Galimberti, D. (Daniela), Rowe, J.B. (James), Finger, E. (Elizabeth), Synofzik, M. (Matthis), Vandenberghe, R. (Rik), De Mendonça, A. (Alexandre), Tagliavini, F. (Fabrizio), Santana, I. (Isabel), Ducharme, S. (Simon), Butler, C. (Chris), Gerhard, A. (Alex), Levin, J. (Johannes), Danek, A. (Adrian), Frisoni, G.B. (Giovanni B.), Sorbi, S. (Sandro), Otto, M. (Markus), Zetterberg, H. (Henrik), Ourselin, S. (Sebastien), Cardoso, M.J. (M. Jorge), and Rohrer, J.D. (Jonathan)

Abstract

Frontotemporal dementia (FTD) is a heterogeneous group of neurodegenerative disorders with both sporadic and genetic forms. Mutations in the progranulin gene (GRN) are a common cause of genetic FTD, causing either a behavioural presentation or, less commonly, language impairment. Presence on T2-weighted images of white matter hyperintensities (WMH) has been previously shown to be more commonly associated with GRN mutations rather than other forms of FTD. The aim of the current study was to investigate the longitudinal change in WMH and the associations of WMH burden with grey matter (GM) loss, markers of neurodegeneration and cognitive function in GRN mutation carriers. 336 participants in the Genetic FTD Initiative (GENFI) study were included in the analysis: 101 presymptomatic and 32 symptomatic GRN mutation carriers, as well as 203 mutation-negative controls. 39 presymptomatic and 12 symptomatic carriers, and 73 controls also had longitudinal data available. Participants underwent MR imaging acquisition including isotropic 1 mm T1-weighted and T2-weighted sequences. WMH were automatically segmented and locally subdivided to enable a more detailed representation of the pathology distribution. Log-transformed WMH volumes were investigated in terms of their global and regional associations with imaging measures (grey matter volumes), biomarker concentrations (plasma neurofilament light chain, NfL, and glial fibrillary acidic protein, GFAP), genetic status (TMEM106B risk genotype) and cognition (tests of executive function). Analyses revealed that WMH load was higher in both symptomatic and presymptomatic groups compared with controls and this load increased over time. In particular, lesions were seen periventricularly in frontal and occipital lobes, progressing to medial layers over time. However, there was variability in the WMH load across GRN mutation carriers – in the symptomatic group 25.0% had none/mild load, 37.5% had medium and 37.5% had a severe load – a di

Published
2019
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10. Bullseye's representation of cerebral white matter hyperintensities

Author

Sudre, C.H., primary, Gomez Anson, B., additional, Davagnanam, I., additional, Schmitt, A., additional, Mendelson, A.F., additional, Prados, F., additional, Smith, L., additional, Atkinson, D., additional, Hughes, A.D., additional, Chaturvedi, N., additional, Cardoso, M.J., additional, Barkhof, F., additional, Jaeger, H.R., additional, and Ourselin, S., additional

Published
2018
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11. Longitudinal multiple sclerosis lesion segmentation: resource & challenge

Author

Carass, A., Roy, S., Jog, A., Cuzzocreo, J.L., Magrath, E., Gherman, A., Button, J., Nguyen, J., Prados, F., Sudre, C.H., Cardoso, M., Cawley, N., Ciccarelli, O., Wheeler-Kingshott, C.A., Ourselin, S., Catanese, L., Deshpande, H., Maurel, P., Commowick, O., Barillot, C., Tomas-Fernandez, X., Warfield, S.K., Vaidya, S., Chunduru, A., Muthuganapathy, R., Krishnamurthi, G., Jesson, A., Arbel, T., Maier, O., Handels, H., Iheme, L.O., Unay, D., Jain, S., Sima, D.M., Smeets, D., Ghafoorian, M., Platel, B., Birenbaum, A., Greenspan, H., Bazin, P.L., Calabresi, P.A., Crainiceanu, C.M., Ellingsen, L.M., Reich, D.S., Prince, J.L., Pham, D.L., Carass, A., Roy, S., Jog, A., Cuzzocreo, J.L., Magrath, E., Gherman, A., Button, J., Nguyen, J., Prados, F., Sudre, C.H., Cardoso, M., Cawley, N., Ciccarelli, O., Wheeler-Kingshott, C.A., Ourselin, S., Catanese, L., Deshpande, H., Maurel, P., Commowick, O., Barillot, C., Tomas-Fernandez, X., Warfield, S.K., Vaidya, S., Chunduru, A., Muthuganapathy, R., Krishnamurthi, G., Jesson, A., Arbel, T., Maier, O., Handels, H., Iheme, L.O., Unay, D., Jain, S., Sima, D.M., Smeets, D., Ghafoorian, M., Platel, B., Birenbaum, A., Greenspan, H., Bazin, P.L., Calabresi, P.A., Crainiceanu, C.M., Ellingsen, L.M., Reich, D.S., Prince, J.L., and Pham, D.L.

Abstract

Contains fulltext : 173122.pdf (Publisher’s version ) (Closed access), In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

Published
2017

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