Automatic coronary artery segmentation based on multi-domains remapping and quantile regression in angiographies.
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.
School of Computer Science and Technology, Harbin Institute of Technology, China; School of Information Engineering, Nanchang University, China.
Zhixun
Z
Li
School of Computer Science and Technology, Harbin Institute of Technology, China. Electronic address: yingtao_hit@163.com.
Yingtao
Y
Zhang
School of Information Engineering, Nanchang University, China.
Huiling
H
Gong
The First Affiliated Hospital, Harbin Medical University, China.
Weimin
W
Li
School of Computer Science and Technology, Harbin Institute of Technology, China.
Xianglong
X
Tang
D000465:Algorithms; D017023:Coronary Angiography / Q000379:methods*; D003331:Coronary Vessels / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D012044:Regression Analysis; D012680:Sensitivity and Specificity
Angiography image; Coronary artery segmentation; Multi-domains remapping; Quantile regression; Reliable boundaries