ArticleScientific reports2017
Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed, 91 citations in OpenAlex.
- Adaptive Optics Image Analysis Using Generative AI (GPT-4) Scripting: Exploratory Study.JMIR formative research · 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
- Non-uniform signal pooling across the foveola.Current biology : CB · 2025Article
- Neural contrast sensitivity is not affected by myopic blur.Scientific reports · 2025Article
- Axicons for improved resolution and depth of focus in adaptive optics scanning light ophthalmoscopy.Biomedical optics express · 2025Article
- 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
- In Vivo Cone Photoreceptor Topography of the Human Foveola.Investigative ophthalmology & visual science · 2025Article
- Neural network assisted annotation and analysis tool to study in-vivo foveolar cone photoreceptor topography.Scientific reports · 2025Article
- Quantification of optical lensing by cellular structures in the living human eye.Biomedical optics express · 2025Article
- Improving cone identification using merged non-confocal quadrant-detection adaptive optics scanning light ophthalmoscope images.Biomedical optics express · 2024Article
- Article
- Reducing manual labeling requirements and improved retinal ganglion cell identification in 3D AO-OCT volumes using semi-supervised learning.Biomedical optics express · 2024Article
- Multimodal deep transfer learning to predict retinal vein occlusion macular edema recurrence after anti-VEGF therapy.Heliyon · 2024Article
- Parafoveal cone function in choroideremia assessed with adaptive optics optoretinography.Scientific reports · 2024Article
- Semi-supervised generative adversarial learning for denoising adaptive optics retinal images.Biomedical optics express · 2024Article
- Alignment, calibration, and validation of an adaptive optics scanning laser ophthalmoscope for high-resolution human foveal imaging.Applied optics · 2024Article
- Cone Density Is Correlated to Outer Segment Length and Retinal Thickness in the Human Foveola.Investigative ophthalmology & visual science · 2023Article
- Deep Density Estimation for Cone Counting and Diagnosis of Genetic Eye Diseases From Adaptive Optics Scanning Light Ophthalmoscope Images.Translational vision science & technology · 2023Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Imaging with an adaptive optics scanning light ophthalmoscope (AOSLO) enables direct visualization of the cone photoreceptor mosaic in the living human retina. Quantitative analysis of AOSLO images typically requires manual grading, which is time consuming, and subjective; thus, automated algorithms are highly desirable. Previously developed automated methods are often reliant on ad hoc rules that may not be transferable between different imaging modalities or retinal locations. In this work, we present a convolutional neural network (CNN) based method for cone detection that learns features of interest directly from training data. This cone-identifying algorithm was trained and validated on separate data sets of confocal and split detector AOSLO images with results showing performance that closely mimics the gold standard manual process. Further, without any need for algorithmic modifications for a specific AOSLO imaging system, our fully-automated multi-modality CNN-based cone detection method resulted in comparable results to previous automatic cone segmentation methods which utilized ad hoc rules for different applications. We have made free open-source software for the proposed method and the corresponding training and testing datasets available online.
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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.