[Automatic Delineation of Clinical Target Volume and Organ at Risk by Deep Learning for Prostate Cancer Adaptive Radiotherapy].

Journal: Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
Published Date: Nov 30, 2022

Abstract

Adaptive radiotherapy can modify the treatment plan online based on the clinical target volume (CTV) and organ at risk (OAR) contours on the cone-beam CT (CBCT) before treatment, improving the accuracy of radiotherapy. However, manual delineation of CTV and OAR on CBCT is time-consuming. In this study, a deep neural network-based method based on U-Net was purposed. CBCT images and corresponding mask were used for model training and validation, showing superior performance in terms of the segmentation accuracy. The proposed method could be used in the clinic to support rapid CTV and OAR contouring for prostate adaptive radiotherapy.

Authors

  • Xinyu Song
    Beijing Institute of Radiation Medicine, Beijing, 100850, China.
  • Xiangyu Zhang
    Department of Geriatrics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Lan Liang
    Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, 610041.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Guangjun Li
    Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China. gjnick829@sina.com.
  • Sen Bai
    Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, PR China. Electronic address: baisen@scu.edu.cn.

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