Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date: Jan 6, 2020

Abstract

PURPOSE: We introduced and evaluated an end-to-end organs-at-risk (OARs) segmentation model that can provide accurate and consistent OARs segmentation results in much less time.

Authors

  • Zhikai Liu
    Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
  • Xia Liu
    Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Science, Haikou 571010, People's Republic of China; Key Laboratory of Monitoring and Control of Tropical Agricultural and Forest Invasive Alien Pests, Ministry of Agriculture, Haikou 571010, People's Republic of China.
  • Bin Xiao
    Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
  • Shaobin Wang
    MedMind Technology Co., Ltd., Beijing 100080, China.
  • Zheng Miao
    Department of Radiology, The First Hospital of Jilin University, No.1, Xinmin Street, Changchun 130021, China (Y.W., M.L., Z.M., J.W., K.H., Q.Y., L.Z., L.M., H.Z.).
  • Yuliang Sun
    Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
  • Fuquan Zhang
    Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.

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