Automated Framework for Detecting Lumen and Media-Adventitia Borders in Intravascular Ultrasound Images.
An automated framework for detecting lumen and media-adventitia borders in intravascular ultrasound images was developed on the basis of an adaptive region-growing method and an unsupervised clustering method. To demonstrate the capability of the framework, linear regression, Bland-Altman analysis and distance analysis were used to quantitatively investigate the correlation, agreement and spatial distance, respectively, between our detected borders and manually traced borders in 337 intravascular ultrasound images in vivo acquired from six patients. The results of these investigations revealed good correlation (r = 0.99), good agreement (>96.82% of results within the 95% confidence interval) and small average distance errors (lumen border: 0.08 mm, media-adventitia border: 0.10 mm) between the borders generated by the automated framework and the manual tracing method. The proposed framework was found to be effective in detecting lumen and media-adventitia borders in intravascular ultrasound images, indicating its potential for use in routine studies of vascular disease.
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, China.
Zhifan
Z
Gao
Institute of Cardiovascular Medicine and Research, LiKaShing Faculty of Medicine, University of Hong Kong, Hong Kong, China.
William Kongto
WK
Hau
Department of Biomedical Engineering, School of Medicine, Shenzhen University, Shenzhen, China.
Minhua
M
Lu
Institute of Clinical Anatomy, Southern Medical University, Guangzhou, China.
Wenhua
W
Huang
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, China. Electronic address: hy.zhang@siat.ac.cn.
Heye
H
Zhang
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, China.
Wanqing
W
Wu
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, China.
Xin
X
Liu
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, China; The Joint Research Centre for Biomedical Engineering, Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong, China.
Yuan-Ting
YT
Zhang
D000465:Algorithms; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D003331:Coronary Vessels / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018084:Ultrasonography, Interventional / Q000379:methods*; D000069558:Unsupervised Machine Learning
Intravascular; Region growing; Ultrasound; Unsupervised clustering