Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer.

Journal: Journal of applied clinical medical physics
Published Date: Nov 22, 2021

Abstract

OBJECTIVES: Because radiotherapy is indispensible for treating cervical cancer, it is critical to accurately and efficiently delineate the radiation targets. We evaluated a deep learning (DL)-based auto-segmentation algorithm for automatic contouring of clinical target volumes (CTVs) in cervical cancers.

Authors

  • Chen-Ying Ma
    Department of Radiation Oncology, First Affiliated Hospital of Soochow University, Suzhou, China.
  • Ju-Ying Zhou
    Department of Radiation Oncology, First Affiliated Hospital of Soochow University, Suzhou, China.
  • Xiao-Ting Xu
    Department of Radiation Oncology, First Affiliated Hospital of Soochow University, Suzhou, China.
  • Jian Guo
    Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China; Clinical Center for Eye Tumors, Capital Medical University, Beijing, 100730, China.
  • Miao-Fei Han
    Shanghai United Imaging Healthcare, Co. Ltd., Jiading, China.
  • Yao-Zong Gao
    Shanghai United Imaging Healthcare, Co. Ltd., Jiading, China.
  • Hui Du
    Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen, China.
  • Johannes N Stahl
    Shanghai United Imaging Healthcare, Co. Ltd., Jiading, China.
  • Jonathan S Maltz
    Shanghai United Imaging Healthcare, Co. Ltd., Jiading, China.

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