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Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images

Research output: Contribution to journalArticle

Christos Bergeles, Adam M. Dubis, Benjamin Davidson, Melissa Kasilian, Angelos Kalitzeos, Joseph Carroll, Alfredo Dubra, Michel Michaelides, Sebastien Ourselin

Original languageEnglish
Article number#288055
JournalBiomedical Optics Express
Issue number6
Early online date26 May 2017
Publication statusPublished - Jun 2017


King's Authors


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.

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