AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy.

Journal: Medical physics
Published Date: Dec 17, 2018

Abstract

PURPOSE: Radiation therapy (RT) is a common treatment option for head and neck (HaN) cancer. An important step involved in RT planning is the delineation of organs-at-risks (OARs) based on HaN computed tomography (CT). However, manually delineating OARs is time-consuming as each slice of CT images needs to be individually examined and a typical CT consists of hundreds of slices. Automating OARs segmentation has the benefit of both reducing the time and improving the quality of RT planning. Existing anatomy autosegmentation algorithms use primarily atlas-based methods, which require sophisticated atlas creation and cannot adequately account for anatomy variations among patients. In this work, we propose an end-to-end, atlas-free three-dimensional (3D) convolutional deep learning framework for fast and fully automated whole-volume HaN anatomy segmentation.

Authors

  • Wentao Zhu
    Department of Computer Science, University of California, Irvine, CA, USA.
  • Yufang Huang
    Lenovo Research, Beijing, China.
  • Liang Zeng
    Departement of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital/Affiliated Cancer Hospital of Xiangya School of Medicine, Changsha 410013, China.
  • Xuming Chen
    Department of Radiation Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yong Liu
    Department of Critical care medicine, Shenzhen Hospital, Southern Medical University, Guangdong, Shenzhen, China.
  • Zhen Qian
    Department of Civil and Environmental Engineering and Heinz College, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
  • Nan Du
    Tencent Medical AI Lab, Palo Alto, CA, USA.
  • Wei Fan
    Department of Epidemiology, School of Public Health, Soochow University, Suzhou 215123, China.
  • Xiaohui Xie
    Department of Computer Science, University of California, Irvine, CA, USA.

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