Markerless Pancreatic Tumor Target Localization Enabled By Deep Learning.

Journal: International journal of radiation oncology, biology, physics
Published Date: Oct 1, 2019

Abstract

PURPOSE: Deep learning is an emerging technique that allows us to capture imaging information beyond the visually recognizable level of a human being. Because of the anatomic characteristics and location, on-board target verification for radiation delivery to pancreatic tumors is a challenging task. Our goal was to use a deep neural network to localize the pancreatic tumor target on kV x-ray images acquired using an on-board imager for image guided radiation therapy.

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.
  • Liyue Shen
    Department of Radiation Oncology, Stanford University, Stanford, California.
  • Bin Han
    2 Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
  • Yong Yang
    Department of Radiation Oncology, Stanford University, CA, USA.
  • Kai Cheng
    Department of Radiation Oncology, Stanford University, Stanford, California.
  • Diego A S Toesca
  • Albert C Koong
    Department of Radiation Oncology, Stanford University School of Medicine , Stanford, California 94305, United States.
  • Daniel T Chang
    Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, California 94305.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.

Keywords

External Resources

Popular Topics
Recent Journals