ArticleBiomedical optics express2022
Retinal layer segmentation in optical coherence tomography (OCT) using a 3D deep-convolutional regression network for patients with age-related macular degeneration.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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.
The trial behind it
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Who cites it
23 citing papers in PubMed.
- Comprehensive 3D Optical Coherence Tomography Dataset for AMD and DME: Facilitating Deep-Learning-Based 3D Segmentation.Scientific data · 2026Article
- ReticularNet: Automated Pixel-Level Segmentation of Reticular Pseudodrusen on Near-Infrared Reflectance Images by Deep Learning.Ophthalmology science · 2026Article
- Construction and Validation of Multi-Omics Predictive Models for Colorectal Cancer Using Machine-Learning Approaches.Pharmacogenomics and personalized medicine · 2026Article
- Evaluation of deep learning-based retinal pigment epithelium segmentation for a widely used optical coherence tomography device.Scientific reports · 2025Article
- Three-Dimensional Quantification of Macular OCT Alterations Improves the Diagnostic Performance of Artificial Intelligence Models.Translational vision science & technology · 2025Article
- Advanced 3D retinal lesion segmentation using channel-spatial attention-guided multi-scale feature aggregation.Biomedical optics express · 2025Article
- OCT-SelfNet: a self-supervised framework with multi-source datasets for generalized retinal disease detection.Frontiers in big data · 2025Article
- Automated Detection of Drusenoid Pigment Epithelial Detachments From Spectral-Domain Optical Coherence Tomography in Patients With AMD.Translational vision science & technology · 2024Article
- Exploring Publicly Accessible Optical Coherence Tomography Datasets: A Comprehensive Overview.Diagnostics (Basel, Switzerland) · 2024Review
- Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve.Biomedical optics express · 2024Article
- Assessment of area and structural irregularity of retinal layers in diabetic retinopathy using machine learning and image processing techniques.Scientific reports · 2024Article
- Deep-learning-based automated measurement of outer retinal layer thickness for use in the assessment of age-related macular degeneration, applicable to both swept-source and spectral-domain OCT imaging.Biomedical optics express · 2024Article
- Automatic exudate and aneurysm segmentation in OCT images using UNET++ and hyperreflective-foci feature based bagged tree ensemble.PloS one · 2024Article
- Deep learning network with differentiable dynamic programming for retina OCT surface segmentation.Biomedical optics express · 2023Article
- A machine learning framework for the quantification of experimental uveitis in murine OCT.Biomedical optics express · 2023Article
- Longitudinal deep network for consistent OCT layer segmentation.Biomedical optics express · 2023Article
- Deep learning segmentation of the tear fluid reservoir under the sclera lens in optical coherence tomography images.Biomedical optics express · 2023Article
- Automated segmentation and quantification of calcified drusen in 3D swept source OCT imaging.Biomedical optics express · 2023Article
- Retinal OCT Layer Segmentation via Joint Motion Correction and Graph-Assisted 3D Neural Network.IEEE access : practical innovations, open solutions · 2023Article
- Depth-resolved visualization and automated quantification of hyperreflective foci on OCT scans using optical attenuation coefficients.Biomedical optics express · 2022Article
Corrections and comments
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Authors and funding
8 authors.
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Abstract
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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.