Multidomain Dependency-Aware Guided Unified-Stage Coronary Artery Branch Recognition Network

Clinical scoring in X-ray coronary angiography image sequences is widely used for revascularization decision-making in cases of coronary artery disease. Accurately recognizing coronary artery branches is a fundamental step in assessing the severity of quantitative stenosis. Existing methods employ a multistage process that includes view separation, skeletonization, graph building, and classification using topological features. However, the graph often suffers from skeleton errors, leading to incorrect topological connections during the classification stage, which requires manual correction. To address these issues, we propose a unified-stage coronary artery branch recognition network (UniCABR) that integrates the segmentation, skeletonization, and graph-building stages. Specifically, we design a dependency-aware module to build dependency graphs in both semantic and spatial domains, avoiding the use of rigid inter-branch topological connections and thus eliminating the need for manual correction of misconnections resulting from skeleton errors. Furthermore, to suppress nontarget branches according to clinical criteria and enhance the performance of side branches, we introduce a small feature supplementation module coupled with an adaptive merged binary supervision method at the pixel level. Extensive experiments on two datasets and a generalization study demonstrate the superiority of UniCABR in performance and generalization ability for coronary artery branch recognition tasks.