Unsupervised Identification of Cone Photoreceptors in Non-confocal Adaptive Optics Scanning Light Ophthalmoscope Images

Overview

Journal Biomed Opt Express

Specialty Radiology

Date 2017 Jul 1

PMID 28663928

Citations 17

Authors

Christos Bergeles

Adam M Dubis

Benjamin Davidson

Melissa Kasilian

Angelos Kalitzeos

Joseph Carroll

Alfredo Dubra

Michel Michaelides

Sebastien Ourselin

Affiliations

Soon will be listed here.

Abstract

Precise measurements of photoreceptor numerosity and spatial arrangement are promising biomarkers for the early detection of retinal pathologies and may be valuable in the evaluation of retinal therapies. Adaptive optics scanning light ophthalmoscopy (AOSLO) is a method of imaging that corrects for aberrations of the eye to acquire high-resolution images that reveal the photoreceptor mosaic. These images are typically graded manually by experienced observers, obviating the robust, large-scale use of the technology. This paper addresses unsupervised automated detection of cones in non-confocal, split-detection AOSLO images. Our algorithm leverages the appearance of split-detection images to create a cone model that is used for classification. Results show that it compares favorably to the state-of-the-art, both for images of healthy retinas and for images from patients affected by Stargardt disease. The algorithm presented also compares well to manual annotation while excelling in speed.

Citing Articles

Improving cone identification using merged non-confocal quadrant-detection adaptive optics scanning light ophthalmoscope images.

Chui T, Migacz J, Muncharaz Duran L, Haq A, Otero-Marquez O, Dubra A Biomed Opt Express. 2024; 15(11):6117-6135.

PMID: 39553865 PMC: 11563324. DOI: 10.1364/BOE.539001.

Adaptive optics scanning laser ophthalmoscopy in a heterogenous cohort with Stargardt disease.

Shah M, Downes S, Smithson H, Young L Sci Rep. 2024; 14(1):23629.

PMID: 39384610 PMC: 11464663. DOI: 10.1038/s41598-024-74088-y.

Characteristics of Rare Inherited Retinal Dystrophies in Adaptive Optics-A Study on 53 Eyes.

Samelska K, Szaflik J, Guszkowska M, Kurowska A, Zaleska-Zmijewska A Diagnostics (Basel). 2023; 13(15).

PMID: 37568834 PMC: 10417470. DOI: 10.3390/diagnostics13152472.

Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes.

Soltanian-Zadeh S, Liu Z, Liu Y, Lassoued A, Cukras C, Miller D Biomed Opt Express. 2023; 14(2):815-833.

PMID: 36874491 PMC: 9979662. DOI: 10.1364/BOE.478693.

Comparison of confocal and non-confocal split-detection cone photoreceptor imaging.

Sredar N, Razeen M, Kowalski B, Carroll J, Dubra A Biomed Opt Express. 2021; 12(2):737-755.

PMID: 33680539 PMC: 7901313. DOI: 10.1364/BOE.403907.

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