Using deep learning for the automated identification of cone and rod photoreceptors from adaptive optics imaging of the human retina.
Journal
Biomedical optics express
ISSN: 2156-7085
Titre abrégé: Biomed Opt Express
Pays: United States
ID NLM: 101540630
Informations de publication
Date de publication:
01 Oct 2022
01 Oct 2022
Historique:
received:
11
07
2022
revised:
13
08
2022
accepted:
16
08
2022
entrez:
25
11
2022
pubmed:
26
11
2022
medline:
26
11
2022
Statut:
epublish
Résumé
Adaptive optics imaging has enabled the enhanced in vivo retinal visualization of individual cone and rod photoreceptors. Effective analysis of such high-resolution, feature rich images requires automated, robust algorithms. This paper describes RC-UPerNet, a novel deep learning algorithm, for identifying both types of photoreceptors, and was evaluated on images from central and peripheral retina extending out to 30° from the fovea in the nasal and temporal directions. Precision, recall and Dice scores were 0.928, 0.917 and 0.922 respectively for cones, and 0.876, 0.867 and 0.870 for rods. Scores agree well with human graders and are better than previously reported AI-based approaches.
Identifiants
pubmed: 36425636
doi: 10.1364/BOE.470071
pii: 470071
pmc: PMC9664895
doi:
Types de publication
Journal Article
Langues
eng
Pagination
5082-5097Informations de copyright
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
Déclaration de conflit d'intérêts
The authors declare that there are no conflicts of interest related to this article.
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