ArticleBiomedical optics express2017
Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images.
Article in Biomedical optics express, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
21 citing papers in PubMed, 32 citations in OpenAlex.
- 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
- Automated cone photoreceptor detection using synthetic data and deep learning in confocal adaptive optics scanning laser ophthalmoscope images.Scientific reports · 2026Article
- Improving cone identification using merged non-confocal quadrant-detection adaptive optics scanning light ophthalmoscope images.Biomedical optics express · 2024Article
- Adaptive optics scanning laser ophthalmoscopy in a heterogenous cohort with Stargardt disease.Scientific reports · 2024Observational
- Characteristics of Rare Inherited Retinal Dystrophies in Adaptive Optics-A Study on 53 Eyes.Diagnostics (Basel, Switzerland) · 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
- Comparison of confocal and non-confocal split-detection cone photoreceptor imaging.Biomedical optics express · 2021Article
- Adaptive optics: principles and applications in ophthalmology.Eye (London, England) · 2021Review
- Intraobserver Repeatability and Interobserver Reproducibility of Foveal Cone Density Measurements inTranslational vision science & technology · 2020Article
- Cellular imaging of inherited retinal diseases using adaptive optics.Eye (London, England) · 2019Review
- RAC-CNN: multimodal deep learning based automatic detection and classification of rod and cone photoreceptors in adaptive optics scanning light ophthalmoscope images.Biomedical optics express · 2019Article
- Adaptive Optics Retinal Imaging in CNGA3-Associated Achromatopsia: Retinal Characterization, Interocular Symmetry, and Intrafamilial Variability.Investigative ophthalmology & visual science · 2019Article
- Article
- Cone Photoreceptor Cell Segmentation and Diameter Measurement on Adaptive Optics Images Using Circularly Constrained Active Contour Model.Investigative ophthalmology & visual science · 2018Article
- Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia.Biomedical optics express · 2018Article
- The Reliability of Cone Density Measurements in the Presence of Rods.Translational vision science & technology · 2018Article
- Automatic Cone Photoreceptor Localisation in Healthy and Stargardt Afflicted Retinas Using Deep Learning.Scientific reports · 2018Article
- Use of focus measure operators for characterization of flood illumination adaptive optics ophthalmoscopy image quality.Biomedical optics express · 2018Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
Precise measurements of photoreceptor numerosity and spatial arrangement are promising biomarkers for the early detection of retinal pathologies and may be valuable in the evaluation of retinal therapies. Adaptive optics scanning light ophthalmoscopy (AOSLO) is a method of imaging that corrects for aberrations of the eye to acquire high-resolution images that reveal the photoreceptor mosaic. These images are typically graded manually by experienced observers, obviating the robust, large-scale use of the technology. This paper addresses unsupervised automated detection of cones in non-confocal, split-detection AOSLO images. Our algorithm leverages the appearance of split-detection images to create a cone model that is used for classification. Results show that it compares favorably to the state-of-the-art, both for images of healthy retinas and for images from patients affected by Stargardt disease. The algorithm presented also compares well to manual annotation while excelling in speed.
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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.