Medical College of Wisconsin
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Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia. Biomed Opt Express 2018 Aug 01;9(8):3740-3756 PMID: 30338152 PMCID: PMC6191607

Pubmed ID





Fast and reliable quantification of cone photoreceptors is a bottleneck in the clinical utilization of adaptive optics scanning light ophthalmoscope (AOSLO) systems for the study, diagnosis, and prognosis of retinal diseases. To-date, manual grading has been the sole reliable source of AOSLO quantification, as no automatic method has been reliably utilized for cone detection in real-world low-quality images of diseased retina. We present a novel deep learning based approach that combines information from both the confocal and non-confocal split detector AOSLO modalities to detect cones in subjects with achromatopsia. Our dual-mode deep learning based approach outperforms the state-of-the-art automated techniques and is on a par with human grading.

Author List

Cunefare D, Langlo CS, Patterson EJ, Blau S, Dubra A, Carroll J, Farsiu S


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

jenkins-FCD Prod-353 9ccd8489072cb19f5b9f808bb23ed672c582f41e