ArticleBiomedical optics express2016
Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images.
Article in Biomedical optics express, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- REPEATABILITY AND LONGITUDINAL ASSESSMENT OF FOVEAL CONE STRUCTURE IN CNGB3-ASSOCIATED ACHROMATOPSIA.Retina (Philadelphia, Pa.) · 2017Trial
- In Vivo Human Cone Photoreceptor Inner Segment Diameter Database Using Adaptive Optics Retinal Imaging.Investigative ophthalmology & visual science · 2026Article
- Open-Source Software Suite for Cell Detection and Segmentation in Adaptive Optics Retinal Imaging.Translational vision science & technology · 2026Article
- Validation of a Rule-Based Automated Method for RPE Cell Detection Using Adaptive Optics Transscleral Flood Illumination.Translational vision science & technology · 2026Article
- Automated cone photoreceptor detection using synthetic data and deep learning in confocal adaptive optics scanning laser ophthalmoscope images.Scientific reports · 2026Article
- Axicons for improved resolution and depth of focus in adaptive optics scanning light ophthalmoscopy.Biomedical optics express · 2025Article
- Deep compressed multichannel adaptive optics scanning light ophthalmoscope.Science advances · 2025Article
- Identifying retinal pigment epithelium cells in adaptive optics-optical coherence tomography images with partial annotations and superhuman accuracy.Biomedical optics express · 2024Article
- Improving cone identification using merged non-confocal quadrant-detection adaptive optics scanning light ophthalmoscope images.Biomedical optics express · 2024Article
- Longitudinal Imaging of the Foveal Cone Mosaic in CNGA3-Associated Achromatopsia.Investigative ophthalmology & visual science · 2024Article
- Minimum intensity projection of embossed quadrant-detection images for improved photoreceptor mosaic visualisation.Frontiers in ophthalmology · 2024Article
- Deep Density Estimation for Cone Counting and Diagnosis of Genetic Eye Diseases From Adaptive Optics Scanning Light Ophthalmoscope Images.Translational vision science & technology · 2023Article
- Adaptive Optics Imaging of Inherited Retinal Disease.Cold Spring Harbor perspectives in medicine · 2023Review
- Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes.Biomedical optics express · 2023Article
- Automatic quantification of cone photoreceptors in adaptive optics scanning light ophthalmoscope images using multi-task learning.Biomedical optics express · 2022Article
- Comparison of Cone Mosaic Metrics From Images Acquired With the SPECTRALIS High Magnification Module and Adaptive Optics Scanning Light Ophthalmoscopy.Translational vision science & technology · 2022Article
- Design of a radial multi-offset detection pattern forBiomedical optics express · 2022Article
- Cone photoreceptor reflectance variation in the northern tree shrew and thirteen-lined ground squirrel.Experimental biology and medicine (Maywood, N.J.) · 2021Article
- Automated image processing pipeline for adaptive optics scanning light ophthalmoscopy.Biomedical optics express · 2021Article
- Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Quantitative analysis of the cone photoreceptor mosaic in the living retina is potentially useful for early diagnosis and prognosis of many ocular diseases. Non-confocal split detector based adaptive optics scanning light ophthalmoscope (AOSLO) imaging reveals the cone photoreceptor inner segment mosaics often not visualized on confocal AOSLO imaging. Despite recent advances in automated cone segmentation algorithms for confocal AOSLO imagery, quantitative analysis of split detector AOSLO images is currently a time-consuming manual process. In this paper, we present the fully automatic adaptive filtering and local detection (AFLD) method for detecting cones in split detector AOSLO images. We validated our algorithm on 80 images from 10 subjects, showing an overall mean Dice's coefficient of 0.95 (standard deviation 0.03), when comparing our AFLD algorithm to an expert grader. This is comparable to the inter-observer Dice's coefficient of 0.94 (standard deviation 0.04). To the best of our knowledge, this is the first validated, fully-automated segmentation method which has been applied to split detector AOSLO images.
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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.