Cybersecurity in the digital world

The structural and functional analysis of coronary arteries in X-ray angiography (XRA) images has great clinical importance for the intraoperative treatment of coronary artery disease. However, due to the complexity of image surroundings, the non-intuition of functional information, and the complex relationships between structure and function, the structural and functional analysis in XRA images is a challenging task. To address these problems, we are proposing an efficient framework, termed AngioSFA. It can effectively associate structural analysis with functional analysis. The AngioSFA mainly consists of a distance transform segmentation network (DTSN) for structural analysis and a dual-view sequential learning network (DSLN) for functional analysis. Specifically, DTSN exploits distance transform in the encoder–decoder architecture to predict the distance map of the artery structure. It considers the geometric property of coronary artery structure to preserve a proper shape prototype. DSLN exploits a dual-view temporal convolutional network to learn the non-intuitive physiological knowledge related to the functional significance from the radius sequences extracted from dual-view XRA images. Furthermore, a commonality sharing module is designed to map the latent embeddings of radius sequences into a shared latent space through orthogonal mapping. Moreover, we introduce a dual-view interaction module to explore the intra-view attentions and the inter-view correlations among the anatomical features of coronary arteries. Extensive experiments on two clinical datasets show that AngioSFA achieves an overall dice of 91.06% for structural analysis and an overall precision of 89.96% for functional analysis. The experimental results and the discussions demonstrate that AngioSFA has a great potential for the efficient intraoperative treatment of coronary artery disease.