基于CTA图像的两阶段U-Net冠状动脉分割

doi:10.12068/j.issn.1005-3026.2022.06.005

东北大学学报（自然科学版） ›› 2022, Vol. 43 ›› Issue (6): 792-800.DOI: 10.12068/j.issn.1005-3026.2022.06.005

基于CTA图像的两阶段U-Net冠状动脉分割

王璐¹，杨小帆¹，王前进¹，徐礼胜^2,3

(1. 东北大学计算机科学与工程学院，辽宁沈阳110169; 2. 东北大学医学与生物信息工程学院，辽宁沈阳110169; 3. 沈阳东软智能医疗科技研究院有限公司，辽宁沈阳110167)

修回日期:2021-07-24 接受日期:2021-07-24 发布日期:2022-07-01
通讯作者: 王璐
作者简介:王璐(1980-)，女，辽宁沈阳人，东北大学副教授；徐礼胜(1975-)，男，安徽安庆人，东北大学教授，博士生导师.
基金资助:
国家自然科学基金资助项目(61773110，U21A20487)；中央高校基本科研业务费专项资金资助项目( N2119008，N181604006)；沈阳市科学技术计划项目(20-201-4-10)；沈阳东软智能医疗科技研究院有限公司会员课题项目(MCMP062002).

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

WANG Lu¹， YANG Xiao-fan¹， WANG Qian-jin¹， XU Li-sheng^2,3

1. School of Computer Science & Engineering， Northeastern University， Shenyang 110169， China; 2. College of Medicine and Biological Information Engineering， Northeastern University， Shenyang 110169， China; 3. Neusoft Research of Intelligent Healthcare Technology Co.， Ltd.， Shenyang 110167， China.

Revised:2021-07-24 Accepted:2021-07-24 Published:2022-07-01
Contact: XU Li-sheng
About author:-
Supported by:
-

摘要/Abstract

摘要： 基于CT血管造影(computed tomography angiography，CTA)图像的冠状动脉自动分割的挑战在于冠状动脉结构复杂、前背景分布严重不平衡，分割时易受冠状静脉和其他组织的干扰.提出了一种两阶段的冠状动脉分割算法，第一阶段采用具有密集特征提取和残差特征修正能力的3D DRU-Net进行分割，保证分割的召回率;在第二阶段提出2D双编码多特征融合U-Net(2D DEMFU-Net)进行细分割，先对原始图像和第一阶段分割结果分别进行特征提取，再采用密集跳跃连接融合两个分支上的多层次语义特征，进一步提高分割准确性.实验结果表明，提出的两阶段分割算法在CortArt2020数据集上的Dice相似系数、召回率和精确度分别优于3D U-Net网络3.83%，5.31%和2.23%.

关键词: CTA图像;冠状动脉分割;两阶段分割算法;U-Net

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

中图分类号:

TP391.41

王璐，杨小帆，王前进，徐礼胜. 基于CTA图像的两阶段U-Net冠状动脉分割[J]. 东北大学学报（自然科学版）, 2022, 43(6): 792-800.

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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基于CTA图像的两阶段U-Net冠状动脉分割

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

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