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1. Why is the Winner the Best?

Author

Eisenmann, M., primary, Reinke, A., additional, Weru, V., additional, Tizabi, M. D., additional, Isensee, F., additional, Adler, T. J., additional, Ali, S., additional, Andrearczyk, V., additional, Aubreville, M., additional, Baid, U., additional, Bakas, S., additional, Balu, N., additional, Bano, S., additional, Bernal, J., additional, Bodenstedt, S., additional, Casella, A., additional, Cheplygina, V., additional, Daum, M., additional, De Bruijne, M., additional, Depeursinge, A., additional, Dorent, R., additional, Egger, J., additional, Ellis, D. G., additional, Engelhardt, S., additional, Ganz, M., additional, Ghatwary, N., additional, Girard, G., additional, Godau, P., additional, Gupta, A., additional, Hansen, L., additional, Harada, K., additional, Heinrich, M., additional, Heller, N., additional, Hering, A., additional, Huaulmé, A., additional, Jannin, P., additional, Kavur, A. E., additional, Kodym, O., additional, Kozubek, M., additional, Li, J., additional, Li, H., additional, Ma, J., additional, Martín-Isla, C., additional, Menze, B., additional, Noble, A., additional, Oreiller, V., additional, Padoy, N., additional, Pati, S., additional, Payette, K., additional, Rädsch, T., additional, Rafael-Patiño, J., additional, Bawa, V. Singh, additional, Speidel, S., additional, Sudre, C. H., additional, Van Wijnen, K., additional, Wagner, M., additional, Wei, D., additional, Yamlahi, A., additional, Yap, M. H., additional, Yuan, C., additional, Zenk, M., additional, Zia, A., additional, Zimmerer, D., additional, Aydogan, D., additional, Bhattarai, B., additional, Bloch, L., additional, Brüngel, R., additional, Cho, J., additional, Choi, C., additional, Dou, Q., additional, Ezhov, I., additional, Friedrich, C. M., additional, Fuller, C., additional, Gaire, R. R., additional, Galdran, A., additional, Faura, Á. García, additional, Grammatikopoulou, M., additional, Hong, S., additional, Jahanifar, M., additional, Jang, I., additional, Kadkhodamohammadi, A., additional, Kang, I., additional, Kofler, F., additional, Kondo, S., additional, Kuijf, H., additional, Li, M., additional, Luu, M., additional, Martinčič, T., additional, Morais, P., additional, Naser, M. A., additional, Oliveira, B., additional, Owen, D., additional, Pang, S., additional, Park, J., additional, Park, S., additional, Płotka, S., additional, Puybareau, E., additional, Rajpoot, N., additional, Ryu, K., additional, Saeed, N., additional, Shephard, A., additional, Shi, P., additional, Štepec, D., additional, Subedi, R., additional, Tochon, G., additional, Torres, H. R., additional, Urien, H., additional, Vilaça, J. L., additional, Wahid, K. A., additional, Wang, H., additional, Wang, J., additional, Wang, L., additional, Wang, X., additional, Wiestler, B., additional, Wodzinski, M., additional, Xia, F., additional, Xie, J., additional, Xiong, Z., additional, Yang, S., additional, Yang, Y., additional, Zhao, Z., additional, Maier-Hein, K., additional, Jäger, P. F., additional, Kopp-Schneider, A., additional, and Maier-Hein, L., additional

Published
2023
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2. Why is the winner the best?

Author

Eisenmann, M, Reinke, A, Weru, V, Tizabi, MD, Isensee, F, Adler, TJ, Ali, S, Andrearczyk, V, Aubreville, M, Baid, U, Bakas, S, Balu, N, Bano, S, Bernal, J, Bodenstedt, S, Casella, A, Cheplygina, V, Daum, M, De Bruijne, M, Depeursinge, A, Dorent, R, Egger, J, Ellis, DG, Engelhardt, S, Ganz, M, Ghatwary, N, Girard, G, Godau, P, Gupta, A, Hansen, L, Harada, K, Heinrich, M, Heller, N, Hering, A, Huaulmé, A, Jannin, P, Kavur, AE, Kodym, O, Kozubek, M, Li, J, Li, H, Ma, J, Martín-Isla, C, Menze, B, Noble, A, Oreiller, V, Padoy, N, Pati, S, Payette, K, Rädsch, T, Rafael-Patiño, J, Bawa, VS, Speidel, S, Sudre, CH, Van Wijnen, K, Wagner, M, Wei, D, Yamlahi, A, Yap, MH, Yuan, C, Zenk, M, Zia, A, Zimmerer, D, Aydogan, D, Bhattarai, B, Bloch, L, Brüngel, R, Cho, J, Choi, C, Dou, Q, Ezhov, I, Friedrich, CM, Fuller, C, Gaire, RR, Galdran, A, García Faura, A, Grammatikopoulou, M, Hong, S, Jahanifar, M, Jang, I, Kadkhodamohammadi, A, Kang, I, Kofler, F, Kondo, S, Kuijf, H, Li, M, Luu, M, Martinčič, T, Morais, P, Naser, MA, Oliveira, B, Owen, D, Pang, S, Park, J, Park, S, Płotka, S, Puybareau, E, Rajpoot, N, Ryu, K, Saeed, N, Eisenmann, M, Reinke, A, Weru, V, Tizabi, MD, Isensee, F, Adler, TJ, Ali, S, Andrearczyk, V, Aubreville, M, Baid, U, Bakas, S, Balu, N, Bano, S, Bernal, J, Bodenstedt, S, Casella, A, Cheplygina, V, Daum, M, De Bruijne, M, Depeursinge, A, Dorent, R, Egger, J, Ellis, DG, Engelhardt, S, Ganz, M, Ghatwary, N, Girard, G, Godau, P, Gupta, A, Hansen, L, Harada, K, Heinrich, M, Heller, N, Hering, A, Huaulmé, A, Jannin, P, Kavur, AE, Kodym, O, Kozubek, M, Li, J, Li, H, Ma, J, Martín-Isla, C, Menze, B, Noble, A, Oreiller, V, Padoy, N, Pati, S, Payette, K, Rädsch, T, Rafael-Patiño, J, Bawa, VS, Speidel, S, Sudre, CH, Van Wijnen, K, Wagner, M, Wei, D, Yamlahi, A, Yap, MH, Yuan, C, Zenk, M, Zia, A, Zimmerer, D, Aydogan, D, Bhattarai, B, Bloch, L, Brüngel, R, Cho, J, Choi, C, Dou, Q, Ezhov, I, Friedrich, CM, Fuller, C, Gaire, RR, Galdran, A, García Faura, A, Grammatikopoulou, M, Hong, S, Jahanifar, M, Jang, I, Kadkhodamohammadi, A, Kang, I, Kofler, F, Kondo, S, Kuijf, H, Li, M, Luu, M, Martinčič, T, Morais, P, Naser, MA, Oliveira, B, Owen, D, Pang, S, Park, J, Park, S, Płotka, S, Puybareau, E, Rajpoot, N, Ryu, K, and Saeed, N

Abstract

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The 'typical' lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

Published
2023

3. Image guidance in neurosurgical procedures, the 'Visages' point of view.

Author

Barillot, C., Coupé, P., El Ganaoui, O., Gibaud, B, Hellier, P., Jannin, P., Paul, P., Prima, S., Wiest-Daesslé, N., Morandi, X., Barillot, C., Coupé, P., El Ganaoui, O., Gibaud, B, Hellier, P., Jannin, P., Paul, P., Prima, S., Wiest-Daesslé, N., and Morandi, X.

Abstract

This paper gives an overview of the evolution of clinical neuroinformatics in the domain of neurosurgery. It shows how image guided neurosurgery (IGNS) is evolving according to the integration of new imaging modalities before, during and after the surgical procedure and how this acts as the premise of the Operative Room of the future. These different issues, as addressed by the VisAGeS INRIA/INSERM U746 research team (http://www.irisa.fr/visages), are presented and discussed in order to exhibit the benefits of an integrated work between physicians (radiologists, neurologists and neurosurgeons) and computer scientists to give adequate answers toward a more effective use of images in IGNS.

Published
2007

6. Comparative Validation of Single-Shot Optical Techniques for Laparoscopic 3-D Surface Reconstruction.

Author

Maier-Hein, L., Groch, A., Bartoli, A., Bodenstedt, S., Boissonnat, G., Chang, P.-L., Clancy, N. T., Elson, D. S., Haase, S., Heim, E., Hornegger, J., Jannin, P., Kenngott, H., Kilgus, T., Muller-Stich, B., Oladokun, D., Rohl, S., dos Santos, T. R., Schlemmer, H.-P., and Seitel, A.

Subjects
LAPAROSCOPY, IMAGE reconstruction, COMPARATIVE studies, SOFT tissue infections, COMPUTERS in medicine, ROBUST control, DIAGNOSIS
Abstract

Intra-operative imaging techniques for obtaining the shape and morphology of soft-tissue surfaces in vivo are a key enabling technology for advanced surgical systems. Different optical techniques for 3-D surface reconstruction in laparoscopy have been proposed, however, so far no quantitative and comparative validation has been performed. Furthermore, robustness of the methods to clinically important factors like smoke or bleeding has not yet been assessed. To address these issues, we have formed a joint international initiative with the aim of validating different state-of-the-art passive and active reconstruction methods in a comparative manner. In this comprehensive in vitro study, we investigated reconstruction accuracy using different organs with various shape and texture and also tested reconstruction robustness with respect to a number of factors like the pose of the endoscope as well as the amount of blood or smoke present in the scene. The study suggests complementary advantages of the different techniques with respect to accuracy, robustness, point density, hardware complexity and computation time. While reconstruction accuracy under ideal conditions was generally high, robustness is a remaining issue to be addressed. Future work should include sensor fusion and in vivo validation studies in a specific clinical context. To trigger further research in surface reconstruction, stereoscopic data of the study will be made publically available at xlink:type="simple" xlink:href="http://www.open-CAS.com" xmlns:xlink="http://www.w3.org/1999/xlink"www.open-CAS.com upon publication of the paper. [ABSTRACT FROM AUTHOR]

Published
2014
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7. A Framework for the Recognition of High-Level Surgical Tasks From Video Images for Cataract Surgeries.

Author

Lalys, F., Riffaud, L., Bouget, D., and Jannin, P.

Subjects
CATARACT surgery, DIAGNOSTIC imaging, OPERATING rooms, VIDEO recording, MARKOV processes, COMPUTER vision
Abstract

The need for a better integration of the new generation of computer-assisted-surgical systems has been recently emphasized. One necessity to achieve this objective is to retrieve data from the operating room (OR) with different sensors, then to derive models from these data. Recently, the use of videos from cameras in the OR has demonstrated its efficiency. In this paper, we propose a framework to assist in the development of systems for the automatic recognition of high-level surgical tasks using microscope videos analysis. We validated its use on cataract procedures. The idea is to combine state-of-the-art computer vision techniques with time series analysis. The first step of the framework consisted in the definition of several visual cues for extracting semantic information, therefore, characterizing each frame of the video. Five different pieces of image-based classifiers were, therefore, implemented. A step of pupil segmentation was also applied for dedicated visual cue detection. Time series classification algorithms were then applied to model time-varying data. Dynamic time warping and hidden Markov models were tested. This association combined the advantages of all methods for better understanding of the problem. The framework was finally validated through various studies. Six binary visual cues were chosen along with 12 phases to detect, obtaining accuracies of 94%. [ABSTRACT FROM PUBLISHER]

Published
2012
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