ArticleScientific data2022
The RETA Benchmark for Retinal Vascular Tree Analysis.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 29 citations in OpenAlex.
- External Validation and Comparison of Automated Retinal Image Quality Grading Methods in the Canadian Longitudinal Study on Aging.Ophthalmology science · 2026Article
- Contextual anatomy-guided deep learning for accurate fovea segmentation in diabetic retinopathy fundus images.Scientific reports · 2026Article
- Uncertainty- and hardness-weighted loss functions for medical image segmentation.Engineering applications of artificial intelligence · 2026Article
- A Fundus Image Dataset for AI-based Artery-Vein Vessel Segmentation.Scientific data · 2025Article
- Confident Learning-Based Label Correction for Retinal Image Segmentation.Diagnostics (Basel, Switzerland) · 2025Article
- VascX Models: Deep Ensembles for Retinal Vascular Analysis From Color Fundus Images.Translational vision science & technology · 2025Article
- Masked Vascular Structure Segmentation and Completion in Retinal Images.IEEE transactions on medical imaging · 2025Article
- Exploring a multi-path U-net with probability distribution attention and cascade dilated convolution for precise retinal vessel segmentation in fundus images.Scientific reports · 2025Article
- Deep learning based approach: automated gingival inflammation grading model using gingival removal strategy.Scientific reports · 2024Article
- MAPLES-DR: MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy.Scientific data · 2024Article
- Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites.Scientific data · 2023Article
- Fractal dimension of retinal vasculature as an image quality metric for automated fundus image analysis systems.Scientific reports · 2022Article
- The RETA Benchmark for Retinal Vascular Tree Analysis.Scientific data · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Topological and geometrical analysis of retinal blood vessels could be a cost-effective way to detect various common diseases. Automated vessel segmentation and vascular tree analysis models require powerful generalization capability in clinical applications. In this work, we constructed a novel benchmark RETA with 81 labelled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow was proposed for vessel annotation task. During database construction, we strived to control inter-annotator and intra-annotator variability by means of multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained other types of annotations including artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities. Subjective and objective quality validations of the annotated vessel masks demonstrated significantly improved quality over the existing open datasets. Our annotation software is also made publicly available serving the purpose of pixel-level vessel visualization. Researchers could develop vessel segmentation algorithms and evaluate segmentation performance using RETA. Moreover, it might promote the study of cross-modality tubular structure segmentation and analysis.
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