Two-Stage U-Net Coronary Artery Segmentation Based on CTA Images

doi:10.12068/j.issn.1005-3026.2022.06.005

Abstract

Abstract: There are many challenges in coronary artery segmentation based on CT angiography(computed tomography angiography)images， such as the complex structure of the coronary artery， the severely unbalanced distribution of the coronary artery and the background， and the susceptibility to interference from the coronary veins and other tissues during segmentation. A two-stage coronary artery segmentation method was proposed. In the first stage， the 3D DRU-Net with dense feature extraction and residual feature correction capabilities was used to ensure the segmentation recall rate. In the second stage， a 2D dual encoding multi-feature fusion U-Net(2D DEMFU-Net)was developed to perform fine segmentation， in which features were extracted from the original image slices and the segmentation results produced by the first stage respectively， and the multi-level semantic features from these two branches were fused using dense skip connections， further improving the accuracy of segmentation. Experimental results show that the proposed two-stage segmentation algorithm outperforms 3D U-Net by 3.83%， 5.31% and 2.23% in terms of Dice similarity coefficient， recall rate and precision， respectively.

Key words: CTA image; coronary artery segmentation; two-stage segmentation; U-Net

CLC Number:

TP391.41

WANG Lu， YANG Xiao-fan， WANG Qian-jin， XU Li-sheng. Two-Stage U-Net Coronary Artery Segmentation Based on CTA Images[J]. Journal of Northeastern University(Natural Science), 2022, 43(6): 792-800.

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