Identifying Retinal Features Using a Self-Configuring CNN for Clinical Intervention
We demonstrate robust segmentation performance and generalizability across independently collected datasets. OCTAVE bridges the gap in publicly available datasets, supporting the development of AI tools for precise disease detection, monitoring, and treatment guidance. This resource has the potential to improve clinical outcomes and advance AI-driven retinal disease management.
Authors
- Stephen T.C. Wong (ORCID: https://orcid.org/0000-0001-9188-6502)
- Wesley Poon
- Matthew Vasquez (ORCID: https://orcid.org/0000-0001-9319-2686)
- Shlomit Schaal (ORCID: https://orcid.org/0000-0002-0002-0445)
- Raksha Raghunathan (ORCID: https://orcid.org/0000-0002-2434-425X)
- Daniel Kermany (ORCID: https://orcid.org/0000-0002-8615-1795)
- Glori Das
- Anaya Bawiskar
- Orhun Davarci
- Natasha Nehra
Institutions
- Texas A&M Health Science Center (US)
- Houston Methodist (US)
- Cornell University (US)
- Texas A&M University (US)
Publication Details
- Journal
- Investigative Ophthalmology & Visual Science
- Published
- 2025-06-17
- DOI
- https://doi.org/10.1167/iovs.66.6.55
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Houston Methodist Hospital
- Houston Methodist Research Institute
- National Cancer Institute