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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • August 2018

Published In

Volume / Issue

  • 9 / 8

Start / End Page

  • 3740 - 3756

PubMed ID

  • 30338152

Pubmed Central ID

  • 30338152

Electronic International Standard Serial Number (EISSN)

  • 2156-7085

International Standard Serial Number (ISSN)

  • 2156-7085

Digital Object Identifier (DOI)

  • 10.1364/BOE.9.003740

Language

  • eng