Corpus ID: 235390655

The Medical Segmentation Decathlon

@article{Antonelli2021TheMS,
  title={The Medical Segmentation Decathlon},
  author={Michela Antonelli and Annika Reinke and Spyridon Bakas and Keyvan Farahani and AnnetteKopp-Schneider and Bennett A. Landman and Geert J. S. Litjens and Bjoern H. Menze and Olaf Ronneberger and Ronald M.Summers and Bram van Ginneken and Michel Bilello and Patrick Bilic and Patrick Ferdinand Christ and Richard K. G. Do and Marc J. Gollub and Stephan Heckers and Henkjan J. Huisman and William R. Jarnagin and Maureen McHugo and Sandy Napel and Jennifer S. Goli Pernicka and Kawal S. Rhode and Catalina Tobon-Gomez and Eugene Vorontsov and James Alastair Meakin and S{\'e}bastien Ourselin and Manuel Wiesenfarth and Pablo Arbel{\'a}ez and Byeonguk Bae and Sihong Chen and Laura Alexandra Daza and Jianjiang Feng and Baochun He and Fabian Isensee and Yuanfeng Ji and Fucang Jia and Namkug Kim and Ildoo Kim and Dorit Merhof and Akshay Pai and Beomhee Park and Mathias Perslev and Ramin Rezaiifar and Oliver Rippel and Ignacio Sarasua and Wei Shen and Jaemin Son and Christian Wachinger and Liansheng Wang and Yan Wang and Yingda Xia and Daguang Xu and Zhanwei Xu and Yefeng Zheng and Amber L. Simpson and Lena Maier-Hein and Manuel Jorge Cardoso},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.05735}
}
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… Expand
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