ArticleBiomedical optics express2018
Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia.
Article in Biomedical optics express, 2018. 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 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
27 citing papers in PubMed, 49 citations in OpenAlex.
- 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
- Minimum intensity projection of embossed quadrant-detection images for improved photoreceptor mosaic visualisation.Frontiers in ophthalmology · 2024Article
- 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
- Foveal Cone Structure in Patients With Blue Cone Monochromacy.Investigative ophthalmology & visual science · 2022Article
- 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
- Open-source deep learning-based automatic segmentation of mouse Schlemm's canal in optical coherence tomography images.Experimental eye research · 2022Article
- Active Cell Appearance Model Induced Generative Adversarial Networks for Annotation-Efficient Cell Segmentation and Identification on Adaptive Optics Retinal Images.IEEE transactions on medical imaging · 2021Article
- Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images.Scientific reports · 2021Article
- Comparison of confocal and non-confocal split-detection cone photoreceptor imaging.Biomedical optics express · 2021Article
- Adaptive optics for high-resolution imaging.Nature reviews. Methods primers · 2021Article
- 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
- Spatially Aware Dense-LinkNet Based Regression Improves Fluorescent Cell Detection in Adaptive Optics Ophthalmic Images.IEEE journal of biomedical and health informatics · 2020Article
- Interocular Symmetry of Foveal Cone Topography in Congenital Achromatopsia.Current eye research · 2020Article
- Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network.Translational vision science & technology · 2020Article
- Intraobserver Repeatability and Interobserver Reproducibility of Foveal Cone Density Measurements inTranslational vision science & technology · 2020Article
- SPATIALLY INFORMED CNN FOR AUTOMATED CONE DETECTION IN ADAPTIVE OPTICS RETINAL IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
- Automatic Detection of Cone Photoreceptors With Fully Convolutional Networks.Translational vision science & technology · 2019Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Fast and reliable quantification of cone photoreceptors is a bottleneck in the clinical utilization of adaptive optics scanning light ophthalmoscope (AOSLO) systems for the study, diagnosis, and prognosis of retinal diseases. To-date, manual grading has been the sole reliable source of AOSLO quantification, as no automatic method has been reliably utilized for cone detection in real-world low-quality images of diseased retina. We present a novel deep learning based approach that combines information from both the confocal and non-confocal split detector AOSLO modalities to detect cones in subjects with achromatopsia. Our dual-mode deep learning based approach outperforms the state-of-the-art automated techniques and is on a par with human grading.
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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.