VP-GAT: vector prior graph attention network for automated segment labeling of coronary arteries

Abstract

Automatic segment labeling of the coronary artery tree is important for computer-aided diagnosis (CAD) of cardiovascular disease. High individual variability among human bodies makes the task very difficult. State-of-the-art methods generally rely on the location information of coronary main branches and image information in a small range, which adversely affects the labeling effect of side branches. We propose a vector prior graph attention network (VP-GAT), which uses image features of organs around the coronary arteries as anatomical prior knowledge, considering the position and direction relationships between segments and surrounding organs. VP-GAT consists of three main parts: image prior GAT, full-vector filed extractor, and image domain prior knowledge extractor. We first extract the anatomical information of the coronary arteries as a full vector field, and then extract the image domain prior knowledge through the hybrid model of ResUnet and Transformer. Finally, we feed the two into the image prior GAT to label the segments. Our method is evaluated on real clinical datasets achieving an F1 score of 95.5%. Extensive experiments show that VP-GAT significantly outperforms state-of-the-art methods in labeling the side branches of coronary arteries.