ArticleBioengineering (Basel, Switzerland)2023
Retinal Vascular Image Segmentation Using Improved UNet Based on Residual Module.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A novel attention mechanism for noise-adaptive and robust segmentation of microtubules in microscopy images.BMC bioinformatics · 2026Article
- CoviSwin: A Deep Vision Transformer for Automatic Segmentation of COVID-19 CT Scans.Bioengineering (Basel, Switzerland) · 2025Article
- FED-UNet++: An Improved Nested UNet for Hippocampus Segmentation in Alzheimer's Disease Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.Diagnostics (Basel, Switzerland) · 2025Review
- VDMNet: A Deep Learning Framework with Vessel Dynamic Convolution and Multi-Scale Fusion for Retinal Vessel Segmentation.Bioengineering (Basel, Switzerland) · 2024Article
- Multi-Layer Preprocessing and U-Net with Residual Attention Block for Retinal Blood Vessel Segmentation.Diagnostics (Basel, Switzerland) · 2023Article
- Multi-scale Bottleneck Residual Network for Retinal Vessel Segmentation.Journal of medical systems · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In recent years, deep learning technology for clinical diagnosis has progressed considerably, and the value of medical imaging continues to increase. In the past, clinicians evaluated medical images according to their individual expertise. In contrast, the application of artificial intelligence technology for automatic analysis and diagnostic assistance to support clinicians in evaluating medical information more efficiently has become an important trend. In this study, we propose a machine learning architecture designed to segment images of retinal blood vessels based on an improved U-Net neural network model. The proposed model incorporates a residual module to extract features more effectively, and includes a full-scale skip connection to combine low level details with high-level features at different scales. The results of an experimental evaluation show that the model was able to segment images of retinal vessels accurately. The proposed method also outperformed several existing models on the benchmark datasets DRIVE and ROSE, including U-Net, ResUNet, U-Net3+, ResUNet++, and CaraNet.
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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.