The Medical Segmentation Decathlon
Creators: name: Antonelli, Michela
name: Reinke, Annika
name: Bakas, Spyridon
name: Farahani, Keyvan
name: AnnetteKopp-Schneider
name: Landman, Bennett A.
name: Litjens, Geert
name: Menze, Bjoern
name: Ronneberger, Olaf
name: Summers, Ronald M.
name: van Ginneken, Bram
name: Bilello, Michel
name: Bilic, Patrick
name: Christ, Patrick F.
name: Do, Richard K. G.
name: Gollub, Marc J.
name: Heckers, Stephan H.
name: Huisman, Henkjan
name: Jarnagin, William R.
name: McHugo, Maureen K.
name: Napel, Sandy
name: Pernicka, Jennifer S. Goli
name: Rhode, Kawal
name: Tobon-Gomez, Catalina
name: Vorontsov, Eugene
name: Huisman, Henkjan
name: Meakin, James A.
name: Ourselin, Sebastien
name: Wiesenfarth, Manuel
name: Arbelaez, Pablo
name: Bae, Byeonguk
name: Chen, Sihong
name: Daza, Laura
name: Feng, Jianjiang
name: He, Baochun
name: Isensee, Fabian
name: Ji, Yuanfeng
name: Jia, Fucang
name: Kim, Namkug
name: Kim, Ildoo
name: Merhof, Dorit
name: Pai, Akshay
name: Park, Beomhee
name: Perslev, Mathias
name: Rezaiifar, Ramin
name: Rippel, Oliver
name: Sarasua, Ignacio
name: Shen, Wei
name: Son, Jaemin
name: Wachinger, Christian
name: Wang, Liansheng
name: Wang, Yan
name: Xia, Yingda
name: Xu, Daguang
name: Xu, Zhanwei
name: Zheng, Yefeng
name: Simpson, Amber L.
name: Maier-Hein, Lena
name: Cardoso, M. Jorge
Publisher: name: arXiv
Year: 2021
Subjects: subject: Image and Video Processing (eess.IV)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Machine Learning (cs.LG)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: 68T07

description: 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.