ArticleScientific reports2019
Deep learning segmentation of major vessels in X-ray coronary angiography.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed, 144 citations in OpenAlex.
- 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
- The current state of artificial intelligence-based invasive coronary angiography image analysis: a systematic review.European heart journal. Digital health · 2026Review
- IUM-hybrid model for enhanced CAD diagnosis using deep learning and VS Grad-CAM visualization.Scientific reports · 2026Article
- A Machine Learning Driven Approach to Quantifying Coronary Artery Tortuosity.JACC. Advances · 2026Article
- Artificial Intelligence Powered Real-Time Coronary Stenosis Recognition and Quantification in Angiography.Journal of imaging informatics in medicine · 2026Article
- A multisource cue fusion-based method for coronary artery stenosis detection in digital subtraction angiography.Journal of thoracic disease · 2026Article
- Angio-fusion net: dual-stream enhanced VGG16 attention U-Net for vessel morphology preservation in XCA segmentation.Frontiers in cardiovascular medicine · 2026Article
- Cardiologist user experience of artificial intelligence-based quantitative coronary angiography.Cardiovascular diagnosis and therapy · 2025Article
- Deep Learning-Based Segmentation of Coronary Arteries and Stenosis Detection in X-Ray Coronary Angiography.JACC. Advances · 2025Article
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
- Utilizing multimodal artificial intelligence to advance cardiovascular diseases.Precision clinical medicine · 2025Review
- Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods.Tomography (Ann Arbor, Mich.) · 2025Review
- Lightweight U-Net for Blood Vessels Segmentation in X-Ray Coronary Angiography.Journal of imaging · 2025Article
- Transformer and Attention-Based Architectures for Segmentation of Coronary Arterial Walls in Intravascular Ultrasound: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- DCA-YOLOv8: A Novel Framework Combined with AICI Loss Function for Coronary Artery Stenosis Detection.Sensors (Basel, Switzerland) · 2024Article
- Review
- Artificial intelligence in interventional radiology: state of the art.European radiology experimental · 2024Review
- Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images.Scientific reports · 2024Article
- Patient-specificFrontiers in cardiovascular medicine · 2024Review
Corrections and comments
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Authors and funding
19 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.
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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.