VMamba-QAG-net: a five-stage pipeline for SYNTAX score computation and decision support in interventional cardiology...

Coronary Artery Disease (CAD) remains a significant worldwide health problem, making accurate evaluation of disease severity essential for treatment planning. The SYNTAX score, derived from X-ray coronary angiography (XCA) images, plays a key role in guiding treatment decisions. However, manual scoring is time-consuming, subject and prone to inter-observer variability, necessitating the development of an objective and reliable system. We proposed VMamba-QAG-Net, a novel five-stage pipeline designed for SYNTAX score estimation directly from XCA images. Stage 1 performs binary vessel segmentation to resolve extreme class imbalance, using a preprocessing of Bilateral Filtering, CLAHE, and Unsharp Masking to enhance vessel boundaries, an EfficientSS2D block with parallel five direction visual mamba scanning and Quantum Attention Gate (QAG) to preserve long-range vascular connectivity. Stage 2, executes multi-class anatomical labelling into 27 distinct coronary artery segments using a four-channel guided input, supported by a Vessel Prototype Memory Bank with metric learning and a centreline-weighted skeleton loss for thin distal structures. Stage 3 and 4 extract ordered vessel centrelines via skeletonization, calculates percentage diameter stenosis through perpendicular ray casting, and detects grade stenotic lesions, using Aquila Optimiser. Stage 5 combines the stenosis information to calculate the SYNTAX score and support treatment decisions in revascularisation. Our proposed model was evaluated on the ARCADE dataset and attained a Dice coefficient of 0.9070 and accuracy of 0.9841 for binary segmentation. For 27-class coronary artery segmentation achieved a Dice coefficient of 0.7512, accuracy of 0.9514, and present class Dice of 0.8214. These results demonstrate the effectiveness of the proposed framework and its potential to support cardiologists in clinical decision-making.