Automated Segmentation of White Matter Hyperintensities on Portable Low-Field Magnetic Resonance Imaging (P9-13.002)
To validate an automated WMH segmentation platform on a large cohort of patients with vascular risk factors.
Authors
- Annabel Sorby‐Adams (ORCID: https://orcid.org/0000-0003-1648-3898)
- Ian Johnson (ORCID: https://orcid.org/0000-0001-5951-6990)
- Alison Champagne
- Adam de Havenon (ORCID: https://orcid.org/0000-0001-8178-8597)
- Seyedmehdi Payabvash (ORCID: https://orcid.org/0000-0003-4628-0370)
- Julia Zabinska
- Dheeraj Lalwani
- Joel A. Smith (ORCID: https://orcid.org/0000-0001-6889-4408)
- Gordon Sze (ORCID: https://orcid.org/0000-0002-1143-8556)
- Julien Gonzalez
- W. Taylor Kimberly (ORCID: https://orcid.org/0000-0002-2519-8530)
- Hailey Brigger (ORCID: https://orcid.org/0009-0007-9075-7662)
- Matthew S. Rosen (ORCID: https://orcid.org/0000-0002-7194-002X)
- Emma Peasley
- Kevin N. Sheth
Institutions
- Harvard University (US)
- Yale University (US)
- Massachusetts General Hospital (US)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
Publication Details
- Journal
- Neurology
- Published
- 2025-04-07
- DOI
- https://doi.org/10.1212/wnl.0000000000208639
- Citations
- 1
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.84