Coronary Artery Segmentation in CTA Images: Evaluating Automated Segmentation of Coronary Arteries Using U-Net Variants and Vesselness Enhancement

Abstract

Accurate segmentation of coronary arteries from Computed Tomography Angiography (CTA) images is crucial for diagnosing and treating cardiovascular diseases, especially with the increasing prevalence of coronary artery disease (CAD), a leading cause of death globally. This paper introduces a comprehensive pipeline that combines vesselness enhancement, heart region of interest (ROI) extraction, and advanced deep learning techniques, specifically the ResUNet model, to efficiently and accurately extract coronary artery vessels. A comparative study of several U-Net-based networks, including U-Net, ResU-Net, Attention U-Net, U-Net++, and TransU-Net, was performed using a 5-fold cross-validation approach. Performance metrics such as Dice Similarity Coefficient (DSC), Jaccard Index (JI), Recall, and Precision were used to assess the models. The results showed that the proposed method achieved a DSC of 0.867, recall of 0.881, and precision of 0.892, outperforming other state-of-the-art methods. Specifically, the ResU-Net model attained a DSC of 0.865 and a JI of 0.764, demonstrating superior segmentation accuracy. These results highlight the potential of automated segmentation techniques to reduce cardiologists' workload, minimize human error, and improve clinical decision-making in CAD management. The proposed method not only enhances the accuracy and efficiency of coronary artery segmentation but also plays a crucial role in the timely diagnosis and effective treatment of cardiovascular diseases.