Automatic coronary artery segmentation based on multi-domains remapping and quantile regression in angiographies. (December 2016)

• Record Type: Journal Article
• Authors: Li, Zhixun; Zhang, Yingtao; Gong, Huiling; Li, Weimin; Tang, Xianglong
• Is Part Of: Computerized medical imaging and graphics. Volume 54(2016)
• Page Start: 55
• Page End: 66
• Publication Date: 2016-12
• Subjects: Reliable boundaries -- Multi-domains remapping -- Quantile regression -- Coronary artery segmentation -- Angiography image; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2016.08.006
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Highlights: A fully automatic coronary artery segmentation method is proposed in angiography. We does a multi-domains remapping method to extract reliable boundary. We does a discrepancy correction via distance balance to handle vessel missing regions. The quantile regression is employed to obtain precise results. The method can obtain good performance for the coronary artery segmentation. 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.