ArticleIEEE transactions on medical imaging2015
Retinal Artery-Vein Classification via Topology Estimation.
Article in IEEE transactions on medical imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- Generalized Retinal Artery/Vein Segmentation via Multi-Dataset Fine-Tuning and Pathology Subgroup Analysis.Bioengineering (Basel, Switzerland) · 2026Article
- A Fundus Image Dataset for AI-based Artery-Vein Vessel Segmentation.Scientific data · 2025Article
- Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network.Science advances · 2025Article
- A new dataset for measuring the performance of blood vessel segmentation methods under distribution shifts.PloS one · 2025Article
- Universal Vessel Segmentation for Multi-Modality Retinal Images.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025Article
- Multi-Task Segmentation and Classification Network for Artery/Vein Classification in Retina Fundus.Entropy (Basel, Switzerland) · 2023Article
- A Global Review of Publicly Available Datasets Containing Fundus Images: Characteristics, Barriers to Access, Usability, and Generalizability.Journal of clinical medicine · 2023Article
- End-to-End Automatic Classification of Retinal Vessel Based on Generative Adversarial Networks with Improved U-Net.Diagnostics (Basel, Switzerland) · 2023Article
- Automated Systems for Calculating Arteriovenous Ratio in Retinographies: A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline.Translational vision science & technology · 2022Article
- State-of-the-art retinal vessel segmentation with minimalistic models.Scientific reports · 2022Article
- A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis.Frontiers in cardiovascular medicine · 2022Article
- MSGANet-RAV: A multiscale guided attention network for artery-vein segmentation and classification from optic disc and retinal images.Journal of optometry · 2022Article
- A Hybrid Method to Enhance Thick and Thin Vessels for Blood Vessel Segmentation.Diagnostics (Basel, Switzerland) · 2021Article
- A Deep Learning Architecture for Vascular Area Measurement in Fundus Images.Ophthalmology science · 2021Article
- Differential artery-vein analysis in quantitative retinal imaging: a review.Quantitative imaging in medicine and surgery · 2021Review
- Vascular morphology and blood flow signatures for differential artery-vein analysis in optical coherence tomography of the retina.Biomedical optics express · 2021Article
- Automatic Artery/Vein Classification Using a Vessel-Constraint Network for Multicenter Fundus Images.Frontiers in cell and developmental biology · 2021Article
- VTG-Net: A CNN Based Vessel Topology Graph Network for Retinal Artery/Vein Classification.Frontiers in medicine · 2021Article
- Artery/vein classification of retinal vessels using classifiers fusion.Health information science and systems · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
We propose a novel, graph-theoretic framework for distinguishing arteries from veins in a fundus image. We make use of the underlying vessel topology to better classify small and midsized vessels. We extend our previously proposed tree topology estimation framework by incorporating expert, domain-specific features to construct a simple, yet powerful global likelihood model. We efficiently maximize this model by iteratively exploring the space of possible solutions consistent with the projected vessels. We tested our method on four retinal datasets and achieved classification accuracies of 91.0%, 93.5%, 91.7%, and 90.9%, outperforming existing methods. Our results show the effectiveness of our approach, which is capable of analyzing the entire vasculature, including peripheral vessels, in wide field-of-view fundus photographs. This topology-based method is a potentially important tool for diagnosing diseases with retinal vascular manifestation.
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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.