Robustness of deep learning segmentation of cardiac substructures in noncontrast computed tomography for breast cancer radiotherapy.

Journal: Medical physics
Published Date: Sep 30, 2021

Abstract

PURPOSE: To develop and evaluate deep learning-based autosegmentation of cardiac substructures from noncontrast planning computed tomography (CT) images in patients undergoing breast cancer radiotherapy and to investigate the algorithm sensitivity to out-of-distribution data such as CT image artifacts.

Authors

  • Xiyao Jin
    Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, China.
  • Maria A Thomas
    Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, 63110, USA.
  • Joseph Dise
    Department of Radiation Oncology, Washington University in St Louis School of Medicine, St. Louis, Missouri, USA.
  • James Kavanaugh
    Department of Radiation Oncology, Washington University in St Louis School of Medicine, St. Louis, Missouri, USA.
  • Jessica Hilliard
    Department of Radiation Oncology, Washington University in St Louis School of Medicine, St. Louis, Missouri, USA.
  • Imran Zoberi
    Department of Radiation Oncology, Washington University, St. Louis, MO 63110, USA.
  • Clifford G Robinson
    Department of Radiation Oncology, Washington University School of Medicine, Saint Louis, Missouri.
  • Geoffrey D Hugo
    Department of Radiation Oncology, Washington University School of Medicine, St. Louis, 63110, MO, USA.

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