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Automatic coronary artery segmentation based on multi-domains remapping and quantile regression in angiographies
Zhixun Li1, Yingtao Zhang2, Huiling Gong3
1School of Computer Science and Technology, Harbin Institute of Technology, China; School of Information Engineering, Nanchang University, China.
Insights
This study introduces a novel automatic coronary artery segmentation method for angiography images. The approach effectively segments complex vascular structures, improving computer-aided diagnosis for coronary artery disease.
Area of Science:
- Medical Imaging
- Cardiovascular Diseases
- Computational Anatomy
Background:
- Coronary artery disease (CAD) is a leading cause of mortality.
- Accurate coronary artery segmentation is crucial for computer-aided diagnosis (CADx) and analysis.
- Existing segmentation techniques struggle with complex vascular textures and manual annotation limitations in coronary angiography.
Purpose of the Study:
- To develop a fully automatic coronary artery segmentation method for angiography images.
- To address challenges posed by complex vascular shapes, overlapping structures, and low contrast regions.
- To provide a robust and clinically practical solution for coronary artery segmentation.
Main Methods:
- A novel method employing multi-domains remapping for reliable boundary identification.
- Robust discrepancy correction utilizing distance balance and quantile regression.
- Application to automatic coronary artery segmentation of angiography images.
Main Results:
- The proposed method demonstrates robust segmentation of overlapping vascular structures.
- Achieves good performance in low contrast regions of coronary angiography.
- Achieved overall segmentation performances: si 95.135%, fnvf 3.733%, fvpf 6.113%, and tpvf 96.268%.
Conclusions:
- The developed automatic segmentation method offers a significant advancement for clinical practice.
- It overcomes limitations of existing methods in handling complex vascular textures and low contrast.
- The approach shows high effectiveness and accuracy in segmenting coronary blood vessels.
Abstract:
Coronary artery disease has become the most dangerous diseases to human life. And coronary artery segmentation is the basis of computer aided diagnosis and analysis. Existing segmentation methods are difficult to handle the complex vascular texture due to the projective nature in conventional coronary angiography. Due to large amount of data and complex vascular shapes, any manual annotation has become increasingly unrealistic. A fully automatic segmentation method is necessary in clinic practice. In this work, we study a method based on reliable boundaries via multi-domains remapping and robust discrepancy correction via distance balance and quantile regression for automatic coronary artery segmentation of angiography images. The proposed method can not only segment overlapping vascular structures robustly, but also achieve good performance in low contrast regions. The effectiveness of our approach is demonstrated on a variety of coronary blood vessels compared with the existing methods. The overall segmentation performances si, fnvf, fvpf and tpvf were 95.135%, 3.733%, 6.113%, 96.268%, respectively.
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