Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images.

Journal: Medical physics
Published Date: Mar 21, 2019

Abstract

PURPOSE: Automatic segmentation of organs-at-risk (OARs) is a key step in radiation treatment planning to reduce human efforts and bias. Deep convolutional neural networks (DCNN) have shown great success in many medical image segmentation applications but there are still challenges in dealing with large 3D images for optimal results. The purpose of this study is to develop a novel DCNN method for thoracic OARs segmentation using cropped 3D images.

Authors

  • Xue Feng
    Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia.
  • Kun Qing
    Radiology & Medical Imaging, University of Virginia, Charlottesville, Virginia, United States of America.
  • Nicholas J Tustison
    a Department of Radiology and Medical Imaging.
  • Craig H Meyer
    Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22903, USA.
  • Quan Chen
    Management School, Zhongshan Institute, University of Electronic Science and Technology of China, Guangdong, 528402, China.

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