Full-head segmentation of MRI with abnormal brain anatomy: model and data release
We are releasing a new state-of-the-art tool for whole-head MRI segmentation in abnormal anatomy, along with the largest volume of labeled clinical head MRIs, including labels for nonbrain structures. Together, the model and data may serve as a benchmark for future efforts.
Authors
- Lukas Hirsch (ORCID: https://orcid.org/0000-0003-3173-9142)
- Abhishek Datta (ORCID: https://orcid.org/0000-0002-6778-8125)
- Adam Jacks (ORCID: https://orcid.org/0000-0001-6358-2878)
- Peter E. Turkeltaub
- Adam Buchwald (ORCID: https://orcid.org/0000-0002-5315-448X)
- Lucas C. Parra (ORCID: https://orcid.org/0000-0003-4667-816X)
- Sampath Kannan
- Andrew M. Birnbaum
Institutions
- University of North Carolina at Chapel Hill (US)
- City College of New York (US)
- Georgetown University (US)
- Soterix Medical (United States) (US)
- New York University (US)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2025-09-17
- DOI
- https://doi.org/10.1117/1.jmi.12.5.054001
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00