Deep multi-scale dilated convolution network for coronary artery segmentation

Automatic segmentation of coronary arteries is of great significance for the rapid and accurate detection of cardiovascular diseases. Currently, deep learning has been successfully applied in the field of coronary artery segmentation. However, the branch structure of coronary arteries is thin, and the contrast between the blood vessels and the background is relatively low, making branches difficult to identify and the false positive rate is high. In response to these challenges, we proposed a multi-scale dilated convolution and deep information extraction network based on unet, which we called 3D-MDCNET. Firstly, adaptive scale expansion convolution modules are designed based on different layers. The advantage is to expand the receptive field and extract a larger range of information, thereby improving the continuity of small branches, while avoiding excessive computational costs. Secondly, the information from different layers of the decoder in Unet is fused with the first-stage segmentation results. Using multi-scale information fusion to enhance information expression, and applying the depth information extraction module to refine the results, effectively reducing the false positive rate. Finally, we introduce deep supervision as a mechanism to mitigate vanishing and exploding gradient problems caused by deep models. By conducting experiments on a benchmark dataset of coronary artery segmentation, our method indeed improves the continuity of small branch segmentation results while reducing the false positive rate. The proposed method has good segmentation performance and generalization ability, outperforming multiple state-of-the-art algorithms on various indicators.