ArticleFrontiers in physiology2023
Automatic coronary artery segmentation of CCTA images using UNet with a local contextual transformer.
Article in Frontiers in physiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Artificial Intelligence-Assisted Intravascular Imaging in Percutaneous Coronary Intervention: Current Status and Advances.Reviews in cardiovascular medicine · 2026Review
- Lightweight Container Orchestration for Reproducible AI and Deep Learning Segmentation of Coronary Arteries and the Aorta in Coronary CT Angiography.Diagnostics (Basel, Switzerland) · 2026Article
- A systematic review of artificial intelligence in radiotherapy associated cardiovascular toxicity.Cardio-oncology (London, England) · 2026Review
- A novel hybrid segmentation method coupled with deep learning for coronary artery extraction from coronary CT angiography.The international journal of cardiovascular imaging · 2026Article
- Effect of SnapShot Freeze 2 on Reducing Pulsation Artifacts in Coronary Artery Imaging of Patients With High Heart Rates.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Deep learning-based assessment of coronary artery disease using curved-multiplanar reconstruction: comprehensive evaluation of stenosis, calcification, and plaque.Frontiers in artificial intelligence · 2026Article
- Seg2RefineNet: a novel DL-based framework for 2D CCTA image-based segmentation and 3D volume-based refinement.Scientific reports · 2025Article
- Few-shot small vessel segmentation using a detail-preserving network enhanced by discriminator.Medical & biological engineering & computing · 2025Article
- Determining the scanning range of coronary computed tomography angiography based on deep learning.World journal of radiology · 2025Article
- Transformer and Attention-Based Architectures for Segmentation of Coronary Arterial Walls in Intravascular Ultrasound: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Unsupervised clustering based coronary artery segmentation.BioData mining · 2025Article
- A Robust Blood Vessel Segmentation Technique for Angiographic Images Employing Multi-Scale Filtering Approach.Journal of clinical medicine · 2025Article
- Improving coronary artery segmentation with self-supervised learning and automated pericoronary adipose tissue segmentation: a multi-institutional study on coronary computed tomography angiography images.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Automatic vessel segmentation and reformation of non-contrast coronary magnetic resonance angiography using transfer learning-based three-dimensional U-net with attention mechanism.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images.Diagnostics (Basel, Switzerland) · 2024Article
- Efficient Extraction of Coronary Artery Vessels from Computed Tomography Angiography Images Using ResUnet and Vesselness.Bioengineering (Basel, Switzerland) · 2024Article
- A comparative analysis of deep learning-based location-adaptive threshold method software against other commercially available software.The international journal of cardiovascular imaging · 2024Article
- Applications and implementation of generative artificial intelligence in cardiovascular imaging with a focus on ethical and legal considerations: what cardiovascular imagers need to know!BJR artificial intelligence · 2024Review
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7 authors.
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Abstract
Coronary artery segmentation is an essential procedure in the computer-aided diagnosis of coronary artery disease. It aims to identify and segment the regions of interest in the coronary circulation for further processing and diagnosis. Currently, automatic segmentation of coronary arteries is often unreliable because of their small size and poor distribution of contrast medium, as well as the problems that lead to over-segmentation or omission. To improve the performance of convolutional-neural-network (CNN) based coronary artery segmentation, we propose a novel automatic method, DR-LCT-UNet, with two innovative components: the Dense Residual (DR) module and the Local Contextual Transformer (LCT) module. The DR module aims to preserve unobtrusive features through dense residual connections, while the LCT module is an improved Transformer that focuses on local contextual information, so that coronary artery-related information can be better exploited. The LCT and DR modules are effectively integrated into the skip connections and encoder-decoder of the 3D segmentation network, respectively. Experiments on our CorArtTS2020 dataset show that the dice similarity coefficient (DSC), Recall, and Precision of the proposed method reached 85.8%, 86.3% and 85.8%, respectively, outperforming 3D-UNet (taken as the reference among the 6 other chosen comparison methods), by 2.1%, 1.9%, and 2.1%.
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