Automatic segmentation of coronary tree in CT angiography images. (9th March 2017)

• Record Type: Journal Article
• Authors: Gao, Zhifan; Liu, Xin; Qi, Song; Wu, Wanqing; Hau, William Kongto; Zhang, Heye
• Other Names: Wong Kelvin guestEditor.; Wang Defeng guestEditor.; Chen Ying guestEditor.
• Is Part Of: International journal of adaptive control and signal processing. Volume 33:Number 8(2019)
• Page Start: 1239
• Page End: 1247
• Publication Date: 2017-03-09
• Subjects: vessel segmentation -- computed tomography angiography -- coronary artery disease; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.2762
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: In this study, we proposed an automatic approach to segment the three‐dimensional coronary tree from computed tomography angiography (CTA) image data. Compared to the previous studies, our approach can efficiently locate the coronary root through extracting aorta by using circular Hough transform. Then, we extracted the two‐dimensional coronary borders in multiple projection directions by dynamic programming and used the border information to reconstruct the coronary surface by the spline fitting. The performance of our approach has been validated on the CTA dataset from 50 subjects, with the comparison to the manual segmentation by an experienced medical physician on the corresponding CTA data and X‐ray angiography data. Our experiments have shown that the average bias between our approach and the manual segmentation are 2.44 mm 2 with confidence interval [‐2.20 mm 2, 6.68 mm 2 ] for cross‐sectional lumen area, 0.84 mm with confidence interval [‐2.76 mm, 4.44 mm] for maximum lumen diameter, and 0.32 mm with confidence interval [‐1.14 mm, 1.78 mm] for minimum lumen diameter. The results can demonstrate the potential effectiveness of our approach in CTA image processing.