Development of a deep learning method to identify acute ischaemic stroke lesions on brain CT
DL methods can be designed for ischaemic lesion detection on CT using the vast quantities of routinely collected brain scans without the need for lesion annotation. Ultimately, this should lead to more robust and widely applicable methods.
Authors
- Paul Armitage (ORCID: https://orcid.org/0000-0003-2790-5478)
- Joanna Marguerite Wardlaw (ORCID: https://orcid.org/0000-0002-9812-6642)
- Amos Storkey (ORCID: https://orcid.org/0000-0002-8100-506X)
- Grant Mair (ORCID: https://orcid.org/0000-0003-2189-443X)
- Antreas Antoniou (ORCID: https://orcid.org/0000-0001-5776-1204)
- Emanuele Trucco (ORCID: https://orcid.org/0000-0002-5055-0794)
- Wenwen Li (ORCID: https://orcid.org/0000-0003-3710-1967)
- Alessandro Fontanella (ORCID: https://orcid.org/0000-0003-4885-9407)
- Eleanor Platt
Institutions
- University of Dundee (GB)
- UK Dementia Research Institute (GB)
- University of Sheffield (GB)
- University of Edinburgh (GB)
Publication Details
- Journal
- Stroke and Vascular Neurology
- Published
- 2024-11-20
- DOI
- https://doi.org/10.1136/svn-2024-003372
- Citations
- 4
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.55
Funders
- UK Research and Innovation
- Edith Murphy Foundation
- UK Dementia Research Institute
- Medical Research Council