A Machine Learning-Based Method for Intracoronary OCT Segmentation and Vulnerable Coronary Plaque Cap Thickness Quantification

Xiaoya Guo, Dalin Tang, David Molony, Chun Yang, Habib Samady, Jie Zheng, Gary S. Mintz, Akiko Maehara, Liang Wang, Xuan Pei, Zhi Yong Li, Genshan Ma, Don P. Giddens

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish
Article number1842008
JournalInternational Journal of Computational Methods
Volume16
Issue number3
DOIs
StatePublished - May 1 2019

Keywords

  • LS-SVM
  • OCT
  • Vulnerable plaque
  • cap thickness
  • coronary
  • segmentation

Fingerprint Dive into the research topics of 'A Machine Learning-Based Method for Intracoronary OCT Segmentation and Vulnerable Coronary Plaque Cap Thickness Quantification'. Together they form a unique fingerprint.

Cite this