CAS-Net: A Novel Coronary Artery Segmentation Neural Network

Abstract

In conventional X-ray coronary angiography, accurate coronary artery segmentation is a crucial and challenging step in the assessment of coronary artery disease. In this paper, we propose a new architecture (CAS-Net) for coronary artery segmentation. It is based on Residual UNet and it includes both channel and spatial attention mechanism in the center part to generate hierarchical rich features of coronary arteries. Experiments are conducted on a private dataset of 150 images. The results show that CAS-Net outperforms the state-of-the-art methods achieving the highest accuracy of 96.91% and Dice of 82.70%.