학술논문
The Medical Segmentation Decathlon
Document Type
article
Author
Michela Antonelli; Annika Reinke; Spyridon Bakas; Keyvan Farahani; Annette Kopp-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. Golia Pernicka; Kawal Rhode; Catalina Tobon-Gomez; Eugene Vorontsov; James A. Meakin; Sebastien Ourselin; Manuel Wiesenfarth; Pablo Arbeláez; Byeonguk Bae; Sihong Chen; Laura Daza; Jianjiang Feng; Baochun He; Fabian Isensee; Yuanfeng Ji; Fucang Jia; Ildoo Kim; Klaus Maier-Hein; 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
Source
Nature Communications, Vol 13, Iss 1, Pp 1-13 (2022)
Subject
Language
English
ISSN
2041-1723
Abstract
International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Here, the authors present the results of a biomedical image segmentation challenge, showing that a method capable of performing well on multiple tasks will generalize well to a previously unseen task.