• 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… 

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