The Medical Segmentation Decathlon

Antonelli M, Reinke A, Bakas S, Farahani K, Kopp-Schneider A, Landman BA, Litjens G, Menze B, Ronneberger O, Summers RM, Van Ginneken B, Bilello M, Bilic P, Christ PF, Do RKG, Gollub MJ, Heckers SH, Huisman H, Jarnagin WR, Mchugo MK, Napel S, Pernicka JSG, Rhode K, Tobon-Gomez C, Vorontsov E, Meakin JA, Ourselin S, Wiesenfarth M, Arbelaez P, Bae B, Chen S, Daza L, Feng J, He B, Isensee F, Ji Y, Jia F, Kim I, Maier-Hein K, Merhof D, Pai A, Park B, Perslev M, Rezaiifar R, Rippel O, Sarasua I, Shen W, Son J, Wachinger C, Wang L, Wang Y, Xia Y, Xu D, Xu Z, Zheng Y, Simpson AL, Maier-Hein L, Cardoso MJ (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Book Volume: 13

Article Number: 4128

Journal Issue: 1

DOI: 10.1038/s41467-022-30695-9

Abstract

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.

Involved external institutions

King’s College London United Kingdom (GB) Deutsches Krebsforschungszentrum (DKFZ) Germany (DE) University of Pennsylvania (UPenn) United States (USA) (US) National Cancer Institute (NCI) United States (USA) (US) Vanderbilt University United States (USA) (US) Radboud University Nijmegen Medical Centre / Radboudumc of voluit Radboud Universitair Medisch Centrum (UMC) Netherlands (NL) University of Zurich / Universität Zürich (UZH) Switzerland (CH) DeepMind Technologies Limited United Kingdom (GB) NIH Clinical Center United States (USA) (US) Technische Universität München (TUM) Germany (DE) Memorial Sloan Kettering Cancer Center United States (USA) (US) Chinese Academy of Sciences (CAS) / 中国科学院 China (CN) Cerebriu A/S Denmark (DK) Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen Germany (DE) VUNO Inc. / 뷰노 Korea, Republic of (KR) University of Copenhagen Denmark (DK) MaaDoTaa United States (USA) (US) Ludwig-Maximilians-Universität (LMU) Germany (DE) Shanghai Jiao Tong University / 上海交通大学 China (CN) Xiamen University China (CN) East China Normal University (ECNU) / 华东师范大学 China (CN) Johns Hopkins University (JHU) United States (USA) (US) Tencent Jarvis Lab / 腾讯天衍实验室 China (CN) Queen's University United Kingdom (GB) Stanford University United States (USA) (US) École Polytechnique de Montréal Canada (CA) Universidad de los Andes Chile (CL) Tsinghua University China (CN) Nvidia Corporation United States (USA) (US)

How to cite

APA:

Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B.A.,... Cardoso, M.J. (2022). The Medical Segmentation Decathlon. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-30695-9

MLA:

Antonelli, Michela, et al. "The Medical Segmentation Decathlon." Nature Communications 13.1 (2022).

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