ArticleBiomedical optics express2022
Using deep learning for the automated identification of cone and rod photoreceptors from adaptive optics imaging of the human retina.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- ISOSNet: a unified framework for cone photoreceptor detection and inner segment and outer segment length measurement from AO-OCT B-scans.Biomedical optics express · 2025Article
- Identifying retinal pigment epithelium cells in adaptive optics-optical coherence tomography images with partial annotations and superhuman accuracy.Biomedical optics express · 2024Article
- Longitudinal Imaging of the Foveal Cone Mosaic in CNGA3-Associated Achromatopsia.Investigative ophthalmology & visual science · 2024Article
- Reducing manual labeling requirements and improved retinal ganglion cell identification in 3D AO-OCT volumes using semi-supervised learning.Biomedical optics express · 2024Article
- Introduction to the Feature Issue on Adaptive Optics for Biomedical Applications.Biomedical optics express · 2023Article
- Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes.Biomedical optics express · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.