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VEA-SegUNet: Edge-Enhanced Multi-Scale Network with F2-Optimization for Robust Coronary Artery Segmentation
Qiuju Yang1, Liangping Yi2, Hang Yi2
1School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China. yangqiuju@snnu.edu.cn.
Insights
VEA-SegUNet improves coronary artery disease diagnosis by enhancing X-ray coronary angiography vessel segmentation. This novel method boosts accuracy and connectivity for better small vessel detection.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiovascular Disease Research
Background:
- Accurate coronary artery disease (CAD) diagnosis relies on precise vessel segmentation in coronary angiography.
- Challenges like low contrast, artifacts, and vessel overlap impede segmentation accuracy and small vessel identification.
Purpose of the Study:
- To introduce VEA-SegUNet, a novel method for improved vessel segmentation in X-ray coronary angiography.
- To address segmentation discontinuities and enhance the detection of small vessels in coronary angiograms.
Main Methods:
- VEA-SegUNet incorporates a vessel enhancement module (VEA) using unsupervised edge detection priors.
- A multi-scale deformable convolutional attention module within the U-Net encoder captures complex vascular structures.
- F2-score optimization and F2 loss are utilized to prioritize vessel connectivity.
Main Results:
- VEA-SegUNet demonstrated superior performance compared to six state-of-the-art U-shaped architectures across multiple datasets (DCA1, CHUAC, XCA).
- Achieved high scores: F1-score 79.1%, F2-score 85.1%, recall 89.3%, IoU 65.1%, accuracy 97.7%, AUC 98.8%.
- Maintained competitive computational efficiency.
Conclusions:
- VEA-SegUNet effectively enhances coronary artery segmentation in X-ray angiography.
- The method shows significant improvements in vessel continuity and small vessel detection.
- VEA-SegUNet is a practical and effective tool for coronary artery segmentation, aiding in CAD diagnosis.
Abstract:
Accurate diagnosis of coronary artery disease (CAD) requires precise vessel segmentation in coronary angiography. However, challenges such as low contrast, imaging artifacts, and vessel overlap often result in segmentation discontinuities and hinder the identification of small vessels. To overcome these issues, we propose VEA-SegUNet, a novel vessel segmentation method for X-ray coronary angiography. Our approach incorporates three key innovations. First, the vessel enhancement module (VEA) leverages unsupervised edge detection priors to emphasize vessel boundaries. Second, a multi-scale deformable convolutional attention module is embedded within the U-Net encoder to capture complex vascular structures at different scales, thereby improving vessel continuity and small vessel detection. Third, F2-score optimization prioritizes vessel connectivity in segmentation topology. We validate the F2-score as a superior metric to the traditional F1-score for coronary segmentation and incorporate F2 loss into the framework. Extensive experiments on the DCA1, CHUAC, and XCA datasets demonstrate that VEA-SegUNet outperforms six state-of-the-art U-shaped architectures on several metrics. It achieves an F1-score of 79.1%, an F2-score of 85.1%, a recall of 89.3%, an IoU of 65.1%, an accuracy of 97.7%, and an AUC of 98.8%, while maintaining a very competitive computational efficiency. These results confirm the effectiveness and practicality of VEA-SegUNet for coronary artery segmentation.
