LWT-ARTERY-LABEL: A Lightweight Framework for Automated Coronary Artery Identification
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915439118909440 |
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| author | Zhang, Shisheng Gharleghi, Ramtin Singh, Sonit Moses, Daniel Adikari, Dona Sowmya, Arcot Beier, Susann |
| author_facet | Zhang, Shisheng Gharleghi, Ramtin Singh, Sonit Moses, Daniel Adikari, Dona Sowmya, Arcot Beier, Susann |
| contents | Coronary artery disease (CAD) remains the leading cause of death globally, with computed tomography coronary angiography (CTCA) serving as a key diagnostic tool. However, coronary arterial analysis using CTCA, such as identifying artery-specific features from computational modelling, is labour-intensive and time-consuming. Automated anatomical labelling of coronary arteries offers a potential solution, yet the inherent anatomical variability of coronary trees presents a significant challenge. Traditional knowledge-based labelling methods fall short in leveraging data-driven insights, while recent deep-learning approaches often demand substantial computational resources and overlook critical clinical knowledge. To address these limitations, we propose a lightweight method that integrates anatomical knowledge with rule-based topology constraints for effective coronary artery labelling. Our approach achieves state-of-the-art performance on benchmark datasets, providing a promising alternative for automated coronary artery labelling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06874 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LWT-ARTERY-LABEL: A Lightweight Framework for Automated Coronary Artery Identification Zhang, Shisheng Gharleghi, Ramtin Singh, Sonit Moses, Daniel Adikari, Dona Sowmya, Arcot Beier, Susann Image and Video Processing Computer Vision and Pattern Recognition Coronary artery disease (CAD) remains the leading cause of death globally, with computed tomography coronary angiography (CTCA) serving as a key diagnostic tool. However, coronary arterial analysis using CTCA, such as identifying artery-specific features from computational modelling, is labour-intensive and time-consuming. Automated anatomical labelling of coronary arteries offers a potential solution, yet the inherent anatomical variability of coronary trees presents a significant challenge. Traditional knowledge-based labelling methods fall short in leveraging data-driven insights, while recent deep-learning approaches often demand substantial computational resources and overlook critical clinical knowledge. To address these limitations, we propose a lightweight method that integrates anatomical knowledge with rule-based topology constraints for effective coronary artery labelling. Our approach achieves state-of-the-art performance on benchmark datasets, providing a promising alternative for automated coronary artery labelling. |
| title | LWT-ARTERY-LABEL: A Lightweight Framework for Automated Coronary Artery Identification |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.06874 |