Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.

Journal: Journal of applied clinical medical physics
Published Date: Aug 9, 2024

Abstract

BACKGROUND: Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed for clinical target volumes (CTVs) of intact and postoperative prostate cancer.

Authors

  • Feng Wen
    Department of Nephrology, Renal Research Institute, Hunan Key Lab of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University Changsha 410011, Hunan, China.
  • Zhebin Chen
    Chengdu Institute of Compute Application, Chinese Academy of Sciences, Chengdu, China.
  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Meng Dou
    Philips Research, Eindhoven, the Netherlands.
  • Jialuo Yang
    Department of Medicine Oncology, Shifang people's Hospital, Shifang, China.
  • Yu Yao
    Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, ‡School of Computer Science and Technology, and §Center of Information Support & Assurance Technology, Anhui University , Hefei, 230601 Anhui, China.
  • Yali Shen
    Department of Radiation Oncology, Cancer Center, the West China Hospital of Sichuan University, Chengdu, China.

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