Deep learning-based automatic segmentation of cardiac substructures for lung cancers.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date:

Abstract

PURPOSE: Accurate and comprehensive segmentation of cardiac substructures is crucial for minimizing the risk of radiation-induced heart disease in lung cancer radiotherapy. We sought to develop and validate deep learning-based auto-segmentation models for cardiac substructures.

Authors

  • Xinru Chen
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States.
  • Raymond P Mumme
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
  • Kelsey L Corrigan
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
  • Yuki Mukai-Sasaki
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; Advanced Medical Center, Shonan Kamakura General Hospital, Kamakura, Japan.
  • Efstratios Koutroumpakis
    Department of Cardiology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States.
  • Nicolas L Palaskas
    Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
  • Callistus M Nguyen
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
  • Yao Zhao
    School of Environmental Science and Engineering, Shaanxi University of Science and Technology, Xi'an 170021, China.
  • Kai Huang
  • Cenji Yu
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States.
  • Ting Xu
    Bioresources Green Transformation Collaborative Innovation Center of Hubei Province, College of Life Sciences, Hubei University, Wuhan 430062, Hubei, China.
  • Aji Daniel
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
  • Peter A Balter
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
  • Xiaodong Zhang
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Joshua S Niedzielski
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States.
  • Sanjay S Shete
    The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
  • Anita Deswal
    Michael E. DeBakey VA Medical Center & Baylor College of Medicine, Houston, TX (B.B., A.D.).
  • Laurence E Court
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Zhongxing Liao
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Jinzhong Yang
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.