A deep learning framework for prostate localization in cone beam CT-guided radiotherapy.

Journal: Medical physics
Published Date:

Abstract

PURPOSE: To develop a deep learning-based model for prostate planning target volume (PTV) localization on cone beam computed tomography (CBCT) to improve the workflow of CBCT-guided patient setup.

Authors

  • Xiaokun Liang
  • Wei Zhao
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.
  • Dimitre H Hristov
    Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
  • Mark K Buyyounouski
    Department of Radiation Oncology, Stanford University, Stanford, California.
  • Steven L Hancock
    Stanford University, Department of Radiation Oncology, Stanford, USA. Electronic address: shancock@stanford.edu.
  • Hilary Bagshaw
    Stanford University, Department of Radiation Oncology, Stanford, USA. Electronic address: hbagshaw@stanford.edu.
  • Qin Zhang
    Department of Burn, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Yaoqin Xie
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.