A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms

Abstract

An accurate and automated segmentation of coronary arteries in X-ray angiograms is essential for cardiologists to diagnose coronary artery disease in clinics. The existing deep learning-based coronary arteries segmentation models focus on using complex networks to improve the accuracy of segmentation while ignoring the computational cost. However, performing such segmentation networks requires a high-performance device with a powerful GPU and a high bandwidth memory. To address this issue, in this study, a lightweight deep learning network is developed for a better balance between computational cost and segmentation accuracy. We have made two efforts in designing the network. On the one hand, we adopt bottleneck residual blocks to replace the internal components in the encoder and decoder of the traditional U-Net to make the network more lightweight. On the other hand, we embed the two attention modules to model long-range dependencies in spatial and channel dimensions for the accuracy of segmentation. In addition, we employ Top-hat transforms and contrast-limited adaptive histogram equalization (CLAHE) as the pre-processing strategy to enhance the coronary arteries to further improve the accuracy. Experimental evaluations conducted on the coronary angiograms dataset show that the proposed lightweight network performs well for accurate coronary artery segmentation, achieving the sensitivity, specificity, accuracy, and area under the curve (AUC) of 0.8770, 0.9789, 0.9729, and 0.9910, respectively. It is noteworthy that the proposed network contains only 0.75 M of parameters, which achieves the best performance by the comparative experiments with popular segmentation networks (such as U-Net with 31.04 M of parameters). Experimental results demonstrate that our network can achieve better performance with an extremely low number of parameters. Furthermore, the generalization experiments indicate that our network can accurately segment coronary angiograms from other coronary angiograms' databases, which demonstrates the strong generalization and robustness of our network.