TY - JOUR

T1 - The Medical Segmentation Decathlon

AU - Antonelli, Michela

AU - Reinke, Annika

AU - Bakas, Spyridon

AU - Farahani, Keyvan

AU - Kopp-Schneider, Annette

AU - Landman, Bennett A.

AU - Litjens, Geert

AU - Menze, Bjoern

AU - Ronneberger, Olaf

AU - Summers, Ronald M.

AU - van Ginneken, Bram

AU - Bilello, Michel

AU - Bilic, Patrick

AU - Christ, Patrick F.

AU - Do, Richard K.G.

AU - Gollub, Marc J.

AU - Heckers, Stephan H.

AU - Huisman, Henkjan

AU - Jarnagin, William R.

AU - McHugo, Maureen K.

AU - Napel, Sandy

AU - Pernicka, Jennifer S.Golia

AU - Rhode, Kawal

AU - Tobon-Gomez, Catalina

AU - Vorontsov, Eugene

AU - Meakin, James A.

AU - Ourselin, Sebastien

AU - Wiesenfarth, Manuel

AU - Arbeláez, Pablo

AU - Bae, Byeonguk

AU - Chen, Sihong

AU - Daza, Laura

AU - Feng, Jianjiang

AU - He, Baochun

AU - Isensee, Fabian

AU - Ji, Yuanfeng

AU - Jia, Fucang

AU - Kim, Ildoo

AU - Maier-Hein, Klaus

AU - Merhof, Dorit

AU - Pai, Akshay

AU - Park, Beomhee

AU - Perslev, Mathias

AU - Rezaiifar, Ramin

AU - Rippel, Oliver

AU - Sarasua, Ignacio

AU - Shen, Wei

AU - Son, Jaemin

AU - Wachinger, Christian

AU - Wang, Liansheng

AU - Wang, Yan

AU - Xia, Yingda

AU - Xu, Daguang

AU - Xu, Zhanwei

AU - Zheng, Yefeng

AU - Simpson, Amber L.

AU - Maier-Hein, Lena

AU - Cardoso, M. Jorge

N1 - Publisher Copyright: © 2022, The Author(s).

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

U2 - 10.1038/s41467-022-30695-9

DO - 10.1038/s41467-022-30695-9

M3 - Journal article

C2 - 35840566

AN - SCOPUS:85134268394

VL - 13

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 4128

ER -