LAUSR

Coronary heart disease is one of the most serious health problems in the world nowadays. By segmenting and examining the coronary arteries in medical images, we can find artery stenosis and plaque, which are the main causes of this certain disease. Segmenting coronary arteries manually is time-consuming and subjective, and the traditional segmentation method requires a good initial point, which is also difficult to apply for the 3D coronary computed tomography angiography (CTA) data. In this paper, we propose a joint framework for coronary CTA segmentation based on deep learning and traditional level set method. A 3D fully convolutional network (FCN) is used to learn the 3D semantic features of coronary arteries, which provides an excellent initial point for the traditional level set. Moreover, an attention gate is added to the entire network, aiming to enhance the vessels and suppress irrelevant regions. The output of 3D FCN with attention gate is optimized by the level set to smooth the boundary to better fit the ground truth segmentation. The proposed algorithm is evaluated by the Jaccard index (JI) and the Dice similarity coefficient (DSC) scores. The experimental results show that the proposed framework provides significant better segmentation results than the vanilla 3D FCN intuitively and quantitively.