Abstract
Accurate coronary vessels segmentation from invasive coronary angiography (ICA) is essential for diagnosis and treatment planning for patients with coronary stenosis. Current machine learning-based approaches primarily utilise convolutional neural networks (CNNs), which heavily focus on the local vessels features and ignore the geometric structures such as the shapes and directions of vessels. This limits the machine understandability of ICA images and creates a bottleneck for improvements of computer-generated segmentation quality, including unstable generalisation ability in low contrast areas and disconnection in vascular structures. To address these issues, we propose a fusion of Graph Attention Network (GAT) and CNN to assist in the learning of global geometric information during coronary vessels segmentation. We train and evaluate the proposed method on a large-scale ICA dataset and demonstrate that combining GAT into a unified network yields improved segmentation performance. Additionally, we utilise specific metrics to demonstrate the achieved improvements, as they offer greater potential for future research and exploration.
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Acknowledgments
The authors acknowledge the use of the facilities and services of the Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford and the use of the University of Oxford Advanced Research Computing (ARC) facility, http://dx.doi.org/10.5281/zenodo.22558, in carrying out this work. AB is a Royal Society University Research Fellow and is supported by the Royal Society Grant No. URF\(\backslash \)R1\(\backslash \)221314. The works of AB, RPC, and VG were partially supported by the British Heart Foundation (BHF) Project under Grant PG/20/21/35082. The work of VG was supported by the CompBioMed 2 Centre of Excellence in Computational Biomedicine (European Commission Horizon 2020 research and innovation programme, grant agreement No. 823712).
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He, H., Banerjee, A., Choudhury, R.P., Grau, V. (2024). Automated Coronary Vessels Segmentation in X-ray Angiography Using Graph Attention Network. In: Camara, O., et al. Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers. STACOM 2023. Lecture Notes in Computer Science, vol 14507. Springer, Cham. https://doi.org/10.1007/978-3-031-52448-6_20
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