Photoreceptor identification and quantitative analysis for the detection of retinal disease in AO-OCT imaging

Elaine M. Wells-Gray; Stacey S. Choi; Matthew Ohr; Colleen M. Cebulla; Nathan Doble

doi:10.1117/12.2510659

8 April 2019 Photoreceptor identification and quantitative analysis for the detection of retinal disease in AO-OCT imaging

Elaine M. Wells-Gray, Stacey S. Choi, Matthew Ohr, Colleen M. Cebulla, Nathan Doble

Author Affiliations +

Proceedings Volume 10858, Ophthalmic Technologies XXIX; 108580O (2019) https://doi.org/10.1117/12.2510659
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

Adaptive optics (AO) enables imaging of cellular structures in the retina that are not visible with clinical imaging techniques, providing the potential for earlier detection of retinal disease and enhanced monitoring of progression. The aim of this work was to develop automated, quantitative techniques for characterizing the morphology of the posterior retina in AO-OCT B-scans. Images were obtained from a custom-built dual-modality AO-optical coherence tomographyscanning laser ophthalmoscopy (AO-OCT-SLO) imaging system. Automated segmentation and cone identification procedures were developed and applied to images from two groups: healthy controls and dry age-related macular degeneration (AMD) subjects. Results from the automated routines were compared to measurements made manually by an expert human reviewer, demonstrating good agreement. Results from the control subjects showed decreasing cone inner and outer segment lengths with increasing distance from the fovea. The cone outer segment tip (COST) layer had a greater variation in axial position compared to the inner segment/outer segment (IS/OS) junction. Results from the AMD group indicate that disruption in the COST layer over drusen occurs earlier and to a greater extent than the IS/OS junction, which may be useful in the detection of emerging drusen.

Conference Presentation

Citation Download Citation

Elaine M. Wells-Gray, Stacey S. Choi, Matthew Ohr, Colleen M. Cebulla, and Nathan Doble "Photoreceptor identification and quantitative analysis for the detection of retinal disease in AO-OCT imaging", Proc. SPIE 10858, Ophthalmic Technologies XXIX, 108580O (8 April 2019); https://doi.org/10.1117/12.2510659

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