Automatic quadriceps and patellae segmentation of MRI with cascaded U -Net and SASSNet deep learning model.

Journal: Medical physics
Published Date: Jan 1, 2022

Abstract

PURPOSE: Automatic muscle segmentation is critical for advancing our understanding of human physiology, biomechanics, and musculoskeletal pathologies, as it allows for timely exploration of large multi-dimensional image sets. Segmentation models are rarely developed/validated for the pediatric model. As such, autosegmentation is not available to explore how muscle architectural changes during development and how disease/pathology affects the developing musculoskeletal system. Thus, we aimed to develop and validate an end-to-end, fully automated, deep learning model for accurate segmentation of the rectus femoris and vastus lateral, medialis, and intermedialis using a pediatric database.

Authors

  • Ruida Cheng
    Scientific Application Services (SAS), Office of Scientific Computing Services (OSCS), Office of Intramural Research, Center of Information Technology, National Institutes of Health, Bethesda, Maryland.
  • Marion Crouzier
  • François Hug
    Nantes University, Laboratory 'Movement, Interactions, Performance' (EA 4334), Nantes, France.
  • Kylie Tucker
    School of Biomedical Sciences, The University of Queensland, Brisbane.
  • Paul Juneau
    NIH Library, Office of Research Services, National Institutes of Health, Bethesda, Maryland.
  • Evan McCreedy
    Scientific Application Services (SAS), Office of Scientific Computing Services (OSCS), Office of Intramural Research, Center of Information Technology, National Institutes of Health, Bethesda, Maryland.
  • William Gandler
    Scientific Application Services (SAS), Office of Scientific Computing Services (OSCS), Office of Intramural Research, Center of Information Technology, National Institutes of Health, Bethesda, Maryland.
  • Matthew J McAuliffe
    Scientific Application Services (SAS), Office of Scientific Computing Services (OSCS), Office of Intramural Research, Center of Information Technology, National Institutes of Health, Bethesda, Maryland.
  • Frances T Sheehan
    Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, Maryland.

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