Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia.

Alfredo Dubra // Publications // Oct 20 2018

PubMed ID: 30338152

Author(s): Cunefare D, Langlo CS, Patterson EJ, Blau S, Dubra A, Carroll J, Farsiu S. Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia. Biomed Opt Express. 2018 Jul 18;9(8):3740-3756. doi: 10.1364/BOE.9.003740. eCollection 2018 Aug 1.

Journal: Biomedical Optics Express, Volume 9, Issue 8, Aug 2018

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.