Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images.

Journal: Medical physics
Published Date: May 6, 2019

Abstract

PURPOSE: Image-guided radiotherapy provides images not only for patient positioning but also for online adaptive radiotherapy. Accurate delineation of organs-at-risk (OARs) on Head and Neck (H&N) CT and MR images is valuable to both initial treatment planning and adaptive planning, but manual contouring is laborious and inconsistent. A novel method based on the generative adversarial network (GAN) with shape constraint (SC-GAN) is developed for fully automated H&N OARs segmentation on CT and low-field MRI.

Authors

  • Nuo Tong
    Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi, 710071, China.
  • Shuiping Gou
    Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi, 710071, China.
  • Shuyuan Yang
    Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi, 710071, China.
  • Minsong Cao
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, 90095, USA.
  • Ke Sheng
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, 90095, USA.

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