The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized t...

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Published in:Nature communications Vol. 13; no. 1; pp. 4128 - 13
Main Authors: Antonelli, Michela, Reinke, Annika, Bakas, Spyridon, Farahani, Keyvan, Kopp-Schneider, Annette, Landman, Bennett A., Litjens, Geert, Menze, Bjoern, Ronneberger, Olaf, Summers, Ronald M., van Ginneken, Bram, Bilello, Michel, Bilic, Patrick, Christ, Patrick F., Do, Richard K. G., Gollub, Marc J., Heckers, Stephan H., Huisman, Henkjan, Jarnagin, William R., McHugo, Maureen K., Napel, Sandy, Pernicka, Jennifer S. Golia, Rhode, Kawal, Tobon-Gomez, Catalina, Vorontsov, Eugene, Meakin, James A., Ourselin, Sebastien, Wiesenfarth, Manuel, Arbeláez, Pablo, Bae, Byeonguk, Chen, Sihong, Daza, Laura, Feng, Jianjiang, He, Baochun, Isensee, Fabian, Ji, Yuanfeng, Jia, Fucang, Kim, Ildoo, Maier-Hein, Klaus, Merhof, Dorit, Pai, Akshay, Park, Beomhee, Perslev, Mathias, Rezaiifar, Ramin, Rippel, Oliver, Sarasua, Ignacio, Shen, Wei, Son, Jaemin, Wachinger, Christian, Wang, Liansheng, Wang, Yan, Xia, Yingda, Xu, Daguang, Xu, Zhanwei, Zheng, Yefeng, Simpson, Amber L., Maier-Hein, Lena, Cardoso, M. Jorge
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 15.07.2022
Nature Publishing Group
Nature Portfolio
Subjects:
692/308/575
692/700/1421/2025
Algorithms
Humanities and Social Sciences
Hypotheses
Image analysis
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Medical imaging
multidisciplinary
Science
Science (multidisciplinary)
State-of-the-art reviews
Training
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Summary:International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training. International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Here, the authors present the results of a biomedical image segmentation challenge, showing that a method capable of performing well on multiple tasks will generalize well to a previously unseen task.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-022-30695-9