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- Title
A Machine Learning-Based Method for Intracoronary OCT Segmentation and Vulnerable Coronary Plaque Cap Thickness Quantification.
- Authors
Guo, Xiaoya; Tang, Dalin; Molony, David; Yang, Chun; Samady, Habib; Zheng, Jie; Mintz, Gary S.; Maehara, Akiko; Wang, Liang; Pei, Xuan; Li, Zhi-Yong; Ma, Genshan; Giddens, Don P.
- Abstract
Accurate cap thickness quantification is of fundamental importance for vulnerable plaque detection in cardiovascular research. A segmentation method for intracoronary optical coherence tomography (OCT) image based on least squares support vector machine (LS-SVM) was performed to characterize plaque component borders and quantify fibrous cap thickness. Manual segmentation of OCT images were performed by experts based on combination of virtual-histology intravascular ultrasound (VH-IVUS) and OCT images and used as gold standard. The segmentation methods based on LS-SVM provided accurate plaque cap thickness (an 8.6% error by LS-SVM vs. 71% error by IVUS50) serving as solid basis for plaque modeling and assessment.
- Subjects
PREDICATE calculus; OPTICAL coherence tomography; INTRAVASCULAR ultrasonography; SUPPORT vector machines; MARKOV random fields; LEAST squares; IMAGE segmentation; DRUG-eluting stents
- Publication
International Journal of Computational Methods, 2019, Vol 16, Issue 3, pN.PAG
- ISSN
0219-8762
- Publication type
Article
- DOI
10.1142/S0219876218420082