Cardiac substructure segmentation with deep learning for improved cardiac sparing.

Journal: Medical physics
Published Date: Dec 29, 2019

Abstract

PURPOSE: Radiation dose to cardiac substructures is related to radiation-induced heart disease. However, substructures are not considered in radiation therapy planning (RTP) due to poor visualization on CT. Therefore, we developed a novel deep learning (DL) pipeline leveraging MRI's soft tissue contrast coupled with CT for state-of-the-art cardiac substructure segmentation requiring a single, non-contrast CT input.

Authors

  • Eric D Morris
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA 90095, USA.
  • Ahmed I Ghanem
    Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, MI, USA.
  • Ming Dong
    Department of Computer Science, Wayne State University.
  • Milan V Pantelic
    Department of Radiology, Henry Ford Cancer Institute, Detroit, MI, USA.
  • Eleanor M Walker
    Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, MI, USA.
  • Carri K Glide-Hurst
    Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA.

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