Automated Coronary Vessels Segmentation in X-ray Angiography Using Graph Attention Network

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.