A deep learning framework for comprehensive segmentation of deep grey nuclei
THOMASINA offers a fast, reproducible, and scalable solution for comprehensive subcortical segmentation from standard T1w MRI. By combining synthetic WMn contrast with state-of-the-art deep learning-based segmentation models, our method addresses key barriers to deployment and sets a foundation for biomarker discovery in clinical and population-scale imaging studies.
Authors
- Manojkumar Saranathan (ORCID: https://orcid.org/0000-0001-9020-9948)
- Ranjani Ramesh
- Alberto Cacciola (ORCID: https://orcid.org/0000-0001-9412-4116)
- Shridhar Singh (ORCID: https://orcid.org/0000-0002-1354-4029)
- Abhinabha Barat
Institutions
- Humanitas University (IT)
- University of Illinois Urbana-Champaign (US)
- University of Massachusetts Chan Medical School (US)
- Cornell University (US)
- IRCCS Humanitas Research Hospital (IT)
Publication Details
- Journal
- medRxiv
- Published
- 2025-12-18
- DOI
- https://doi.org/10.64898/2025.12.16.25342423
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00