Deep network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water–fat separated MRI
Fusion of multiple anatomical mappings from multiple MAS techniques enriches the template set and improves the segmentation accuracy. Additional fusion with deep network decisions applied to the subject space offers complementary information. The proposed approach can produce accurate segmentation of individual muscle groups in 3D thigh MRI scans.
Authors
- Rita Rastogi Kalyani (ORCID: https://orcid.org/0000-0002-1530-6884)
- Luigi Ferrucci (ORCID: https://orcid.org/0000-0002-6273-1613)
- Nagasoujanya Annasamudram
- Qi Yang (ORCID: https://orcid.org/0000-0002-5773-0456)
- Azubuike Okorie (ORCID: https://orcid.org/0000-0002-1348-3166)
- Richard G. Spencer (ORCID: https://orcid.org/0000-0001-7101-4328)
- Sokratis Makrogiannis (ORCID: https://orcid.org/0000-0003-0316-3529)
- Bennett A. Landman (ORCID: https://orcid.org/0000-0001-5733-2127)
Institutions
- Delaware State University (US)
- National Institutes of Health (US)
- Johns Hopkins University (US)
- Vanderbilt University (US)
- Johns Hopkins Medicine (US)
- Institute on Aging (US)
- National Institute on Aging (US)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2024-09-03
- DOI
- https://doi.org/10.1117/1.jmi.11.5.054003
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.21