ArticleIEEE access : practical innovations, open solutions2021
A Computationally Efficient Approach to Segmentation of the Aorta and Coronary Arteries Using Deep Learning.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 63 citations in OpenAlex.
- A Computational Pipeline for Patient-Specific Modeling of Thoracic Aortic Aneurysm: From Medical Image to Finite Element Analysis.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- MJournal of imaging · 2025Article
- Seg2RefineNet: a novel DL-based framework for 2D CCTA image-based segmentation and 3D volume-based refinement.Scientific reports · 2025Article
- Improving Coronary Artery Disease Diagnosis in Cardiac MRI with Self-Supervised Learning.Diagnostics (Basel, Switzerland) · 2025Article
- 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
- Automatic 3D Segmentation and Identification of Anomalous Aortic Origin of the Coronary Arteries Combining Multi-view 2D Convolutional Neural Networks.Journal of imaging informatics in medicine · 2024Article
- Automatic Detection and Classification of Hypertensive Retinopathy with Improved Convolution Neural Network and Improved SVM.Bioengineering (Basel, Switzerland) · 2024Article
- Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.Journal of cardiovascular development and disease · 2023Review
- Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes.Journal of clinical medicine · 2023Review
- A U-Shaped Network Based on Multi-level Feature and Dual-Attention Coordination Mechanism for Coronary Artery Segmentation of CCTA Images.Cardiovascular engineering and technology · 2023Article
- A benchmark study of convolutional neural networks in fully automatic segmentation of aortic root.Frontiers in bioengineering and biotechnology · 2023Article
- Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET.Diagnostics (Basel, Switzerland) · 2022Review
- Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique.Diagnostics (Basel, Switzerland) · 2022Article
- Automated Identification of Coronary Arteries in Assisting Inexperienced Readers: Comparison between Two Commercial Vendors.Diagnostics (Basel, Switzerland) · 2022Article
- Review
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
Early detection and diagnosis of coronary artery disease could reduce the risk of developing a heart attack. The coronary arteries are optimally visualised using computed tomography coronary angiography (CTCA) imaging. These images are reviewed by specialist radiologists who evaluate the coronary arteries for potential narrowing. A lack of radiologists in the UK is a constraint to timely diagnosis of coronary artery disease, particularly in the acute accident and emergency department setting. The development of automated methods by which coronary artery narrowing can be identified rapidly and accurately are therefore timely. Such complex computer based tools also need to be sufficiently computationally efficient that they can run on servers typically found in hospital settings, where graphical processing units for example are unavailable. We propose a fully automatic two-dimensional Unet model to segment the aorta and coronary arteries on CTCA images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively. Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published two-dimensional or three-dimensional deep learning models. Importantly, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed without requiring graphical processing units, and therefore can be used in a hospital setting.
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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.