ArticleJournal of medical imaging (Bellingham, Wash.)2019
Retinal vessel segmentation using dense U-net with multiscale inputs.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 35 citations in OpenAlex.
- Article
- 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
- DEAF-Net: Detail-Enhanced Attention Feature Fusion Network for Retinal Vessel Segmentation.Journal of imaging informatics in medicine · 2025Article
- Multi scale multi attention network for blood vessel segmentation in fundus images.Scientific reports · 2025Article
- Article
- MCSE-U-Net: multi-convolution blocks and squeeze and excitation blocks for vessel segmentation.Quantitative imaging in medicine and surgery · 2024Article
- Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review.Ophthalmology and therapy · 2023Review
- HT-Net: A Hybrid Transformer Network for Fundus Vessel Segmentation.Sensors (Basel, Switzerland) · 2022Article
- Retinal Vessel Segmentation Based on B-COSFIRE Filters in Fundus Images.Frontiers in public health · 2022Article
- Generative Adversarial Network Combined with SE-ResNet and Dilated Inception Block for Segmenting Retinal Vessels.Computational intelligence and neuroscience · 2022Article
- BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease.Frontiers in artificial intelligence · 2020Article
- Applications of Deep Learning: Automated Assessment of Vascular Tortuosity in Mouse Models of Oxygen-Induced Retinopathy.Ophthalmology scienceArticle
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A color fundus image is an image of the inner wall of the eyeball taken with a fundus camera. Doctors can observe retinal vessel changes in the image, and these changes can be used to diagnose many serious diseases such as atherosclerosis, glaucoma, and age-related macular degeneration. Automated segmentation of retinal vessels can facilitate more efficient diagnosis of these diseases. We propose an improved U-net architecture to segment retinal vessels. Multiscale input layer and dense block are introduced into the conventional U-net, so that the network can make use of richer spatial context information. The proposed method is evaluated on the public dataset DRIVE, achieving 0.8199 in sensitivity and 0.9561 in accuracy. Especially for thin blood vessels, which are difficult to detect because of their low contrast with the background pixels, the segmentation results have been improved.
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