Towards accurate coronary artery calcium segmentation with multi‐scale attention mechanism

Abstract

Coronary artery calcium is a strong and independent marker of atherosclerosis and cardiovascular disease. Typically, the accurate segmentation of computed tomography images of the chest is an important prerequisite and basis for coronary artery calcium identification and analysis. However, this is very challenging in practice because the boundaries of coronary artery calcium, the small lesions with large shape variation, are very blurry, resulting in poor performance in existing studies. To tackle this challenge, we present a novel Attention-based Multi-Scale Network called AMSN, which can process information through both the main and boundary branches in parallel. Key to our AMSN is a new non-local multi-scale context encoder module, which is mainly composed of the multi-scale attention mechanism and local global long short-term memory module. By aggregating the multi-scale context information, i.e. high-resolution low-level and low-resolution high-level features, the model's feature representative capability and deployment ability are improved effectively. Besides, we introduce a new boundary preserving loss, which can consider the boundary information of all coronary artery calcium together and establish links for the segmentation of different coronary artery calcium simultaneously. Extensive experiments demonstrate our AMSN enables reliable accurate coronary artery calcium segmentation for assisted cardiovascular disease diagnosis clinically.