Incorporating imaging information from deep neural network layers into image guided radiation therapy (IGRT).

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Nov 1, 2019

Abstract

BACKGROUND AND PURPOSE: To investigate a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine projection kilovoltage (kV) X-ray images in image-guided radiation therapy (IGRT).

Authors

  • 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.
  • Bin Han
    2 Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
  • Yong Yang
    Department of Radiation Oncology, Stanford University, CA, USA.
  • Mark Buyyounouski
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China.
  • 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.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.

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