Deep Learning Technique for Automatic Segmentation of Proximal Hip Musculoskeletal Tissues From CT Scan Images: A MrOS Study
Our deep learning-based technique offers a fast and accurate method for segmentation and quantification of musculoskeletal tissues in proximal hip, with potential clinical value.
Authors
- Mahdi Imani (ORCID: https://orcid.org/0000-0003-1374-5709)
- Eric Orwoll (ORCID: https://orcid.org/0000-0002-8520-7355)
- Erik Meijering (ORCID: https://orcid.org/0000-0001-8015-8358)
- Thâng Dao (ORCID: https://orcid.org/0000-0003-4814-2545)
- Sara Vogrin (ORCID: https://orcid.org/0000-0002-1597-9421)
- Peggy M. Cawthon (ORCID: https://orcid.org/0000-0003-4938-9478)
- Gustavo Duque (ORCID: https://orcid.org/0000-0001-8126-0637)
- Timothy Kwok (ORCID: https://orcid.org/0000-0001-9253-3549)
- Jared Buratto (ORCID: https://orcid.org/0000-0003-0178-0216)
Institutions
- Oregon Health & Science University (US)
- The University of Melbourne (AU)
- Chinese University of Hong Kong (HK)
- University of California, San Francisco (US)
- McGill University Health Centre (CA)
- California Pacific Medical Center (US)
- UNSW Sydney (AU)
- Hong Kong Jockey Club (HK)
- Western Health (AU)
- Australian Institute for Musculoskeletal Science (AU)
- McGill University (CA)
Publication Details
- Journal
- Journal of Cachexia Sarcopenia and Muscle
- Published
- 2025-02-28
- DOI
- https://doi.org/10.1002/jcsm.13728
- Citations
- 5
- Primary Topic
- Hip disorders and treatments
- Type
- article
- Field-Weighted Citation Impact
- 10.03
Funders
- National Institutes of Health
- National Institute on Aging
- National Institute of Arthritis and Musculoskeletal and Skin Diseases
- National Center for Advancing Translational Sciences