Open AccessAutomated stent coverage analysis in intravascular OCT (IVOCT) image volumes using a support vector machine and mesh growing
Author(s) -
Hong Lu,
Juhwan Lee,
Soumya Ray,
Kentaro Tanaka,
Hiram G. Bezerra,
Andrew M. Rollins,
David L. Wilson
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.10.002809
Subject(s) - computer science , stent , robustness (evolution) , support vector machine , artificial intelligence , optical coherence tomography , pattern recognition (psychology) , computer vision , radiology , medicine , biology , biochemistry , gene
Absence of vascular-stent tissue coverage by IVOCT is a biomarker for potential stent-related thrombosis. We developed highly-automated algorithms to classify covered and uncovered struts and quantitatively evaluate stent apposition. We trained a machine learning model on 7,125 images, and included an active learning, relabeling step to improve noisy labels. We obtained uncovered strut classification sensitivity/specificity (94%/90%) comparable to analyst inter-and-intra-observer variability and AUC (0.97), and tissue coverage thickness measurement arguably better than the commercial product. By comparing classification models from regular and relabeled data sets, we observed robustness of the support vector machine to noisy data. A graph-based algorithm detected clusters of uncovered struts thought to pose a greater risk than isolated uncovered struts. The software enables highly-automated, objective, repeatable, comprehensive stent analysis.