Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images.

Journal: Radiation oncology (London, England)
PMID: 32690103

Abstract

BACKGROUND: Impaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed to compare a deep learning model with a commercial atlas-based model for fast auto-segmentation of the masticatory muscles on head and neck computed tomography (CT) images.

Authors

  • Wen Chen
    School of Cyber Science and Engineering, Sichuan University, Chengdu, Sichuan, China.
  • Yimin Li
    Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.
  • Brandon A Dyer
    Department of Radiation Oncology, University of Washington, Seattle, WA, United States.
  • Xue Feng
    Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia.
  • Shyam Rao
    Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.
  • Stanley H Benedict
    Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.
  • Quan Chen
    Management School, Zhongshan Institute, University of Electronic Science and Technology of China, Guangdong, 528402, China.
  • Yi Rong
    Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.

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