The Medical Segmentation Decathlon
- URL: http://arxiv.org/abs/2106.05735v1
- Date: Thu, 10 Jun 2021 13:34:06 GMT
- Title: The Medical Segmentation Decathlon
- Authors: Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani,
AnnetteKopp-Schneider, Bennett A. Landman, Geert Litjens, Bjoern Menze, Olaf
Ronneberger, Ronald M.Summers, Bram van Ginneken, Michel Bilello, Patrick
Bilic, Patrick F. Christ, Richard K. G. Do, Marc J. Gollub, Stephan H.
Heckers, Henkjan Huisman, William R. Jarnagin, Maureen K. McHugo, Sandy
Napel, Jennifer S. Goli Pernicka, Kawal Rhode, Catalina Tobon-Gomez, Eugene
Vorontsov, Henkjan Huisman, James A. Meakin, Sebastien Ourselin, Manuel
Wiesenfarth, Pablo Arbelaez, Byeonguk Bae, Sihong Chen, Laura Daza, Jianjiang
Feng, Baochun He, Fabian Isensee, Yuanfeng Ji, Fucang Jia, Namkug Kim, Ildoo
Kim, Dorit Merhof, Akshay Pai, Beomhee Park, Mathias Perslev, Ramin
Rezaiifar, Oliver Rippel, Ignacio Sarasua, Wei Shen, Jaemin Son, Christian
Wachinger, Liansheng Wang, Yan Wang, Yingda Xia, Daguang Xu, Zhanwei Xu,
Yefeng Zheng, Amber L. Simpson, Lena Maier-Hein, M. Jorge Cardoso
- Abstract summary: State-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks.
A consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks.
The training of accurate AI segmentation models is now commoditized to non AI experts.
- Score: 37.44481677534694
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: International challenges have become the de facto standard for comparative
assessment of image analysis algorithms given a specific task. Segmentation is
so far the most widely investigated medical image processing task, but the
various segmentation challenges have typically been organized in isolation,
such that algorithm development was driven by the need to tackle a single
specific clinical problem. We hypothesized 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. To investigate the
hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a
biomedical image analysis challenge, in which algorithms compete in a multitude
of both tasks and modalities. The underlying data set was designed to explore
the axis of difficulties typically encountered when dealing with medical
images, such as small data sets, unbalanced labels, multi-site data and small
objects. The MSD challenge confirmed that algorithms with a consistent good
performance on a set of tasks preserved their good average performance on a
different set of previously unseen tasks. Moreover, by monitoring the MSD
winner for two years, we found that this algorithm continued generalizing well
to a wide range of other clinical problems, further confirming our hypothesis.
Three main conclusions can be drawn from this study: (1) state-of-the-art image
segmentation algorithms are mature, accurate, and 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 non AI
experts.
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