Improving cone identification using merged non-confocal quadrant-detection adaptive optics scanning light ophthalmoscope images
Cone photoreceptor inner segments visualized in non-confocal split-detection adaptive optics scanning light ophthalmoscope (AOSLO) images appear as obliquely illuminated domes with bright and dark opposing regions. Previously, the pairing of these bright and dark regions for automated photoreceptor identification has necessitated complex algorithms. Here we demonstrate how the merging of split-detection images captured with a non-confocal quadrant light detection scheme allows automated cone identification using simple, open-source image processing tools, while also improving accuracy in both normal and pathologic retinas.
Authors
- Alfredo Dubra (ORCID: https://orcid.org/0000-0002-6506-9020)
- Justin Migacz (ORCID: https://orcid.org/0000-0002-3045-1502)
- Richard B. Rosen (ORCID: https://orcid.org/0000-0001-9643-3510)
- Oscar Otero-Marquez (ORCID: https://orcid.org/0000-0003-1591-9952)
- Toco Yuen Ping Chui (ORCID: https://orcid.org/0000-0003-2060-3848)
- Luis Muncharaz Duran
- Affan Haq (ORCID: https://orcid.org/0009-0001-8235-9169)
Institutions
- New York Eye and Ear Infirmary (US)
- Stanford University (US)
Publication Details
- Journal
- Biomedical Optics Express
- Published
- 2024-09-23
- DOI
- https://doi.org/10.1364/boe.539001
- Citations
- 2
- Primary Topic
- Ophthalmology and Visual Impairment Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.77
Funders
- Research to Prevent Blindness
- Marrus Family Foundation
- National Heart, Lung, and Blood Institute
- National Eye Institute