Fully-automated segmentation of muscle and inter-/intra-muscular fat from magnetic resonance images of calves and thighs: an open-source workflow in Python
The greatest challenge in segmenting IMF from MRI is in selecting a dynamic threshold that consistently performs across repeated imaging. Fully-automated ITSA achieved this, demonstrated low short- and long-term precision error, conducive of use within RCTs.
Authors
- Kenneth Tam
- Shannon Reitsma (ORCID: https://orcid.org/0000-0003-4332-3128)
- Sarah Costa (ORCID: https://orcid.org/0009-0004-3209-6227)
- Éva Szabó (ORCID: https://orcid.org/0000-0001-8306-6977)
- Hana Gillick
- Si Wen Liu
- Andy Kin On Wong (ORCID: https://orcid.org/0000-0001-8540-8854)
- Jonathan D. Adachi (ORCID: https://orcid.org/0000-0001-9142-2767)
Institutions
- University Health Network (CA)
- University of Toronto (CA)
- Toronto Rehabilitation Institute (CA)
- Toronto General Hospital (CA)
- Arthritis Society (CA)
- Public Health Ontario (CA)
- University of California, Davis (US)
- McMaster University (CA)
Publication Details
- Journal
- Skeletal Muscle
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1186/s13395-024-00365-z
- Citations
- 2
- Primary Topic
- Cardiovascular Disease and Adiposity
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- Arthritis Society
- Canadian Institutes of Health Research