@article{1434201,
      recid = {1434201},
      author = {Andrearczyk, Vincent, and Oreiller, Valentin, and  Depeursinge, Adrien,},
      title = {Head and neck tumor segmentation : First Challenge,  HECKTOR 2020, held in conjunction with MICCAI 2020, Lima,  Peru, October 4, 2020, proceedings /. 3D Head and Neck  Tumor Segmentation in PET/CT Challenge},
      pages = {1 online resource (x, 109 pages) :},
      abstract = {This book constitutes the First 3D Head and Neck Tumor  Segmentation in PET/CT Challenge, HECKTOR 2020, which was  held in conjunction with the 23rd International Conference  on Medical Image Computing and Computer-Assisted  Intervention, MICCAI 2020, in Lima, Peru, in October 2020.  The challenge took place virtually due to the COVID-19  pandemic. The 2 full and 8 short papers presented together  with an overview paper in this volume were carefully  reviewed and selected form numerous submissions. This  challenge aims to evaluate and compare the current  state-of-the-art methods for automatic head and neck tumor  segmentation. In the context of this challenge, a dataset  of 204 delineated PET/CT images was made available for  training as well as 53 PET/CT images for testing. Various  deep learning methods were developed by the participants  with excellent results.},
      url = {http://library.usi.edu/record/1434201},
      doi = {https://doi.org/10.1007/978-3-030-67194-5},
}