Corpus ID: 67855790

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

@article{Simpson2019ALA,
  title={A large annotated medical image dataset for the development and evaluation of segmentation algorithms},
  author={Amber L. Simpson and Michela Antonelli and Spyridon Bakas and Michel Bilello and Keyvan Farahani and Bram van Ginneken and Annette Kopp-Schneider and Bennett A. Landman and Geert J. S. Litjens and Bjoern H. Menze and Olaf Ronneberger and Ronald M. Summers and Patrick Bilic and Patrick Ferdinand Christ and Richard Kinh Gian Do and Marc J. Gollub and Jennifer Golia-Pernicka and Stephan Heckers and William R. Jarnagin and Maureen McHugo and Sandy Napel and Eugene Vorontsov and Lena Maier-Hein and M. Jorge Cardoso},
  journal={ArXiv},
  year={2019},
  volume={abs/1902.09063}
}
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation… Expand
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