Automated 3D segmentation of rotator cuff muscle and fat from longitudinal CT for shoulder arthroplasty evaluation.

Journal: Skeletal radiology
Published Date: Aug 9, 2025

Abstract

OBJECTIVE: To develop and validate a deep learning model for automated 3D segmentation of rotator cuff muscles on longitudinal CT scans to quantify muscle volume and fat fraction in patients undergoing total shoulder arthroplasty (TSA).

Authors

  • Mingrui Yang
    Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, Ohio, USA.
  • Bong-Jae Jun
    Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.
  • Tammy Owings
    Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.
  • Nikhil Subhas
    Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.
  • Joshua Polster
    Department of Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
  • Carl S Winalski
    Imaging Institute, Cleveland Clinic, Cleveland, OH.
  • Jason C Ho
    Department of Orthopaedics, Cleveland Clinic, Cleveland, OH, USA.
  • Vahid Entezari
    Department of Orthopaedics, Cleveland Clinic, Cleveland, OH, USA.
  • Kathleen A Derwin
    Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.
  • Eric T Ricchetti
    Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, OH, USA.
  • Xiaojuan Li
    Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, Ohio, USA.

Keywords

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