Cunefare, D;
Langlo, CS;
Patterson, EJ;
Blau, S;
Dubra, A;
Carroll, J;
Farsiu, S;
(2018)
Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia.
Biomedical Optics Express
, 9
(8)
p. 3740.
10.1364/boe.9.003740.
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Abstract
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.
Type: | Article |
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Title: | Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1364/boe.9.003740 |
Publisher version: | https://doi.org/10.1364/BOE.9.003740 |
Language: | English |
Additional information: | © 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement (https://doi.org/10.1364/OA_License_v1). |
Keywords: | Image analysis; Pattern recognition, neural networks; Ophthalmology; (110.1080) Active or adaptive optics |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology |
URI: | https://discovery.ucl.ac.uk/id/eprint/10086712 |
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