Automated 3D segmentation of rotator cuff muscle and fat from longitudinal CT for shoulder arthroplasty evaluation
This study developed accurate and reliable deep learning models for automated 3D segmentation of rotator cuff muscles on clinical CT scans in TSA patients. These models substantially reduce the time required for muscle volume and fat fraction analysis and provide a practical tool for investigating how rotator cuff muscle health relates to surgical outcomes. This has the potential to inform patient selection, rehabilitation planning, and surgical decision-making in TSA and RCR.
Authors
- Eric T. Ricchetti (ORCID: https://orcid.org/0000-0002-4371-7998)
- Naveen Subhas (ORCID: https://orcid.org/0000-0002-0502-6280)
- Bong Jae Jun (ORCID: https://orcid.org/0000-0003-3283-6622)
- Vahid Entezari (ORCID: https://orcid.org/0000-0002-3796-3084)
- Kathleen A. Derwin (ORCID: https://orcid.org/0000-0001-7753-0366)
- Jason Ho (ORCID: https://orcid.org/0000-0003-2096-2752)
- Carl S. Winalski (ORCID: https://orcid.org/0000-0002-1101-0402)
- Mingrui Yang (ORCID: https://orcid.org/0000-0002-8902-6316)
- Xiaojuan Li (ORCID: https://orcid.org/0000-0002-0567-9935)
- Tammy M. Owings
- Joshua M. Polster
Institutions
- Cleveland Clinic (US)
- Cleveland Clinic Lerner College of Medicine (US)
Publication Details
- Journal
- Skeletal Radiology
- Published
- 2025-08-09
- DOI
- https://doi.org/10.1007/s00256-025-04991-6
- Primary Topic
- Shoulder Injury and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Cleveland Clinic
- National Institutes of Health
- National Institute of Arthritis and Musculoskeletal and Skin Diseases