Chronic Subdural Hematoma Segmentation: A Dedicated Model to Overcome the Limitations of Acute Hemorrhage Segmentation Across Chronic Subdural Hematoma Subtypes and Density Variations
: 0.931 ± 0.046; P < .001). The proposed deep learning model enables more accurate and reliable segmentation of CSDH across hematoma subtypes and densities.
Authors
- Peter Kamel (ORCID: https://orcid.org/0000-0001-6810-9801)
- David Dreizin (ORCID: https://orcid.org/0000-0002-0176-0912)
- Thorsten R. Fleiter
- Bhavya Reddy
- Ritvik Mutyam
Institutions
- University of Maryland, Baltimore (US)
- The University of Texas MD Anderson Cancer Center (US)
- George Mason University (US)
Publication Details
- Journal
- Journal of Imaging Informatics in Medicine
- Published
- 2026-04-14
- DOI
- https://doi.org/10.1007/s10278-026-01952-w
- Primary Topic
- Neurosurgical Procedures and Complications
- Type
- article
- Field-Weighted Citation Impact
- 0.00