Automatic segmentation of high-risk clinical target volume and organs at risk in brachytherapy of cervical cancer with a convolutional neural network.

Journal: Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PMID: 39147623

Abstract

PURPOSE: This study aimed to design an autodelineation model based on convolutional neural networks for generating high-risk clinical target volumes and organs at risk in image-guided adaptive brachytherapy for cervical cancer.

Authors

  • J Zhu
    Department of Thyroid and Breast Surgery, the 960th Hospital of the People's Liberation Army of China, Jinan 250031, China.
  • J Yan
    Department of Cardiology, First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China.
  • J Zhang
    Department of Mechanical Engineering, Columbia University, 500 West 120th Street, New York, NY 10027, USA.
  • L Yu
    Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China.
  • A Song
    Department of Radiation Oncology, Cangzhou Central Hospital, Cangzhou, Hebei 061001, China.
  • Z Zheng
    Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China; Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China. Electronic address: 13437860260@163.com.
  • Y Chen
  • S Wang
    Bruker Optics Inc, Fremont, CA, United States of America.
  • Q Chen
    ShuKun (Beijing) Network Technology Co., Limited, Shanghai, China.
  • Z Liu
    School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China.
  • F Zhang
    Department of General Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China.

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