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Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images. Biomed Opt Express 2017 Jun 01;8(6):3081-3094 PMID: 28663928 PMCID: PMC5480451

Pubmed ID

28663928

DOI

10.1364/BOE.8.003081

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.

Author List

Bergeles C, Dubis AM, Davidson B, Kasilian M, Kalitzeos A, Carroll J, Dubra A, Michaelides M, Ourselin S

Author

Joseph J. Carroll PhD Director, Professor in the Ophthalmology and Visual Sciences department at Medical College of Wisconsin




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