Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration.

Journal: Magnetic resonance in medicine
Published Date: Feb 6, 2023

Abstract

PURPOSE: Fast and accurate thigh muscle segmentation from MRI is important for quantitative assessment of thigh muscle morphology and composition. A novel deep learning (DL) based thigh muscle and surrounding tissues segmentation model was developed for fully automatic and reproducible cross-sectional area (CSA) and fat fraction (FF) quantification and tested in patients at 10 years after anterior cruciate ligament reconstructions.

Authors

  • Sibaji Gaj
    Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio, United States of America.
  • Brendan L Eck
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106.
  • Dongxing Xie
    Department of Orthopaedics, Xiangya Hospital, Central South University, Changsha, 410008, China.
  • Richard Lartey
    Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, Ohio, USA.
  • Charlotte Lo
    Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, Ohio, USA.
  • William Zaylor
    Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, Ohio, USA.
  • Mingrui Yang
    Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, Ohio, USA.
  • Kunio Nakamura
    Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio, United States of America.
  • Carl S Winalski
    Imaging Institute, Cleveland Clinic, Cleveland, OH.
  • Kurt P Spindler
    Cleveland Clinic Sports Health Center, Garfield Heights, Ohio, USA.
  • Xiaojuan Li
    Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, Ohio, USA.

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