Semantic segmentation of coronary arteries in computed tomography angiograph: A multi-center, multi-vendor and multi disease study

Abstract

Coronary artery disease (CAD) is one of the primary causes of death worldwide accountings for almost 25% of all deaths worldwide each year. Accurate diagnosis and assessment of CAD and stenosis require the semantic segmentation of the coronary arteries and aorta in coronary computed tomography angiography (CCTA) images, which is of great significance. Nonetheless, producing semantic segmentations is particularly challenging due to the anatomical similarity between various surrounding areas, the branching of the arteries which includes numerous bifurcations, and the presence of smaller vessels that add to the complexity. Reducing the resolution of a 3D image to fit within the constraints of available GPU memory can result in a loss of detail, which is often undesirable. Instead, using patches of the image as input can help mitigate this issue. In this study, we propose a novel semantic segmentation method based on the 3D U-Net that uses three different datasets consisting of multi-center, multi-vendor, and multi-continent data CCTA images. We achieved accuracies of 91.2%, 93.9%, and 97.3%, respectively. The outcomes of the study demonstrate the efficacy of the method for accurately segmenting the aorta and/or the coronary arteries.