ArticleBiomedical optics express2019
RAC-CNN: multimodal deep learning based automatic detection and classification of rod and cone photoreceptors in adaptive optics scanning light ophthalmoscope images.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The trial behind it
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Who cites it
27 citing papers in PubMed.
- In Vivo Human Cone Photoreceptor Inner Segment Diameter Database Using Adaptive Optics Retinal Imaging.Investigative ophthalmology & visual science · 2026Article
- 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
- Changes in Waveguiding Cone Photoreceptors and Color Vision in Patients With Diabetes Mellitus.Investigative ophthalmology & visual science · 2024Article
- Identifying retinal pigment epithelium cells in adaptive optics-optical coherence tomography images with partial annotations and superhuman accuracy.Biomedical optics express · 2024Article
- Reducing manual labeling requirements and improved retinal ganglion cell identification in 3D AO-OCT volumes using semi-supervised learning.Biomedical optics express · 2024Article
- Clinical Application of Adaptive Optics Imaging in Diagnosis, Management, and Monitoring of Ophthalmological Diseases: A Narrative Review.Medical science monitor : international medical journal of experimental and clinical research · 2023Review
- 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
- Wide-Field Three-Dimensional Depth-Invariant Cellular-Resolution Imaging of the Human Retina.Small (Weinheim an der Bergstrasse, Germany) · 2023Article
- Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes.Biomedical optics express · 2023Article
- Extracting spacing-derived estimates of rod density in healthy retinae.Biomedical optics express · 2023Article
- Using deep learning for the automated identification of cone and rod photoreceptors from adaptive optics imaging of the human retina.Biomedical optics express · 2022Article
- Intergrader agreement of foveal cone topography measured using adaptive optics scanning light ophthalmoscopy.Biomedical optics express · 2022Article
- Artificial intelligence-based strategies to identify patient populations and advance analysis in age-related macular degeneration clinical trials.Experimental eye research · 2022Article
- Theoretical versus empirical measures of retinal magnification for scaling AOSLO images.Journal of the Optical Society of America. A, Optics, image science, and vision · 2021Article
- Deep learning classification of cervical dysplasia using depth-resolved angular light scattering profiles.Biomedical optics express · 2021Article
- Promises and pitfalls of evaluating photoreceptor-based retinal disease with adaptive optics scanning light ophthalmoscopy (AOSLO).Progress in retinal and eye research · 2021Review
- Automated image processing pipeline for adaptive optics scanning light ophthalmoscopy.Biomedical optics express · 2021Article
- Article
- Open-Source Automatic Segmentation of Ocular Structures and Biomarkers of Microbial Keratitis on Slit-Lamp Photography Images Using Deep Learning.IEEE journal of biomedical and health informatics · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Quantification of the human rod and cone photoreceptor mosaic in adaptive optics scanning light ophthalmoscope (AOSLO) images is useful for the study of various retinal pathologies. Subjective and time-consuming manual grading has remained the gold standard for evaluating these images, with no well validated automatic methods for detecting individual rods having been developed. We present a novel deep learning based automatic method, called the rod and cone CNN (RAC-CNN), for detecting and classifying rods and cones in multimodal AOSLO images. We test our method on images from healthy subjects as well as subjects with achromatopsia over a range of retinal eccentricities. We show that our method is on par with human grading for detecting rods and cones.
Identifiers
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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.