ArticleScientific reports2021
AngioNet: a convolutional neural network for vessel segmentation in X-ray angiography.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 96 citations in OpenAlex.
- A standardized workflow for precision liver resection: systematic review of integrated 3D visualization, indocyanine green fluorescence, and augmented reality navigation.Journal of robotic surgery · 2026Pooled it
- Deep learning-based treatment decision support framework for multi-vessel coronary artery disease using integrated coronary angiography and clinical data.BMC medical informatics and decision making · 2026Article
- IUM-hybrid model for enhanced CAD diagnosis using deep learning and VS Grad-CAM visualization.Scientific reports · 2026Article
- VEA-SegUNet: Edge-Enhanced Multi-Scale Network with F2-Optimization for Robust Coronary Artery Segmentation.Journal of imaging informatics in medicine · 2026Article
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- Texture-Based Preprocessing Framework with nnU-Net Model for Accurate Intracranial Artery Segmentation.Journal of imaging · 2025Article
- Deep Learning-Based Segmentation of Coronary Arteries and Stenosis Detection in X-Ray Coronary Angiography.JACC. Advances · 2025Article
- Deep Learning Models for Automatic Classification of Anatomic Location in Abdominopelvic Digital Subtraction Angiography.Journal of imaging informatics in medicine · 2025Article
- Validation of UniverSeg for Interventional Abdominal Angiographic Segmentation.Journal of imaging informatics in medicine · 2025Article
- Accuracy of segment anything model for classification of vascular stenosis in digital subtraction angiography.CVIR endovascular · 2025Article
- The Laryngovibrogram as a normalized spatiotemporal representation of vocal fold dynamics.Scientific reports · 2025Article
- Lightweight U-Net for Blood Vessels Segmentation in X-Ray Coronary Angiography.Journal of imaging · 2025Article
- Optimized aortic root segmentation during transcatheter aortic valve implantation.Frontiers in cardiovascular medicine · 2025Article
- Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney.Scientific reports · 2024Article
- Deep Learning for 3D Vascular Segmentation in Phase Contrast Tomography.Research square · 2024Article
- Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images.Scientific reports · 2024Article
- Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images.Scientific reports · 2024Article
- Optimizing cardiovascular image segmentation through integrated hierarchical features and attention mechanisms.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Patient-specificFrontiers in cardiovascular medicine · 2024Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 3 countries.
Funding
Abstract
Coronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye is flushed through the coronary vessels to visualize the severity of vessel narrowing, or stenosis. Cardiologists typically use visual estimation to approximate the percent diameter reduction of the stenosis, and this directs therapies like stent placement. A fully automatic method to segment the vessels would eliminate potential subjectivity and provide a quantitative and systematic measurement of diameter reduction. Here, we have designed a convolutional neural network, AngioNet, for vessel segmentation in X-ray angiography images. The main innovation in this network is the introduction of an Angiographic Processing Network (APN) which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, pixel accuracy 0.983, sensitivity 0.918, specificity 0.987). The purpose of the APN is to create an end-to-end pipeline for image pre-processing and segmentation, learning the best possible pre-processing filters to improve segmentation. We have also demonstrated the interchangeability of our network in measuring vessel diameter with Quantitative Coronary Angiography. Our results indicate that AngioNet is a powerful tool for automatic angiographic vessel segmentation that could facilitate systematic anatomical assessment of coronary stenosis in the clinical workflow.
Identifiers
What OpenQuestion holds
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.