Deep learning-based multiclass segmentation in aneurysmal subarachnoid hemorrhage
Deep learning enables automated multiclass segmentation of aneurysmal subarachnoid hemorrhage-related pathologies and achieves performance approaching that of a human rater. This enables automatized volumetries of pathologies identified on admission CTs in patients with subarachnoid hemorrhage potentially leading to imaging biomarkers for improved outcome prediction.
Authors
- Peter Vajkoczy (ORCID: https://orcid.org/0000-0003-4350-392X)
- Fujimaro Ishida (ORCID: https://orcid.org/0000-0001-6324-027X)
- Adam Hilbert (ORCID: https://orcid.org/0000-0003-3447-5453)
- Orhun Utku Aydin (ORCID: https://orcid.org/0000-0002-8808-7651)
- Satoru Tanioka (ORCID: https://orcid.org/0000-0002-4678-6163)
- Ahmed A. Khalil (ORCID: https://orcid.org/0000-0003-1752-4305)
- Dietmar Frey (ORCID: https://orcid.org/0000-0001-5407-2331)
- Nora F. Dengler (ORCID: https://orcid.org/0000-0001-7783-8053)
- Julia Kiewitz
- Marie Gultom
- Anouar Nouri
Institutions
- Humboldt-Universität zu Berlin (DE)
- Helios Hospital Bad Saarow (DE)
- Mie Chuo Medical Center (JP)
- Freie Universität Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2024-12-13
- DOI
- https://doi.org/10.3389/fneur.2024.1490216
- Citations
- 8
- Primary Topic
- Intracranial Aneurysms: Treatment and Complications
- Type
- article
- Field-Weighted Citation Impact
- 2.95
Funders
- European Commission
- Berlin Institute of Health
- HORIZON EUROPE Framework Programme