Automated stent coverage analysis in intravascular OCT (IVOCT) image volumes using a support vector machine and mesh growing.

Hong Lu,Kentaro Tanaka,Andrew M Rollins,Juhwan Lee,Soumya Ray,David L Wilson,Hiram G Bezerra

doi:10.1364/boe.10.002809

Abstract

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.

Highlights

Stent implantation via percutaneous coronary intervention is the preferred coronary revascularization procedure for patients with a flow-limiting atherosclerotic lesion
Some studies have reported that bioresorbable stent (BRS) has a higher risk of device thrombosis than metallic stents [7,8,9,10,11,12,13], since thick stent struts are strongly correlated with blood-flow alterations and thrombogenicity
We used the probabilistic output of Support vector machine (SVM) to plot the receiver operating characteristic (ROC) curve (Fig. 5)

Summary

Introduction

Stent implantation via percutaneous coronary intervention is the preferred coronary revascularization procedure for patients with a flow-limiting atherosclerotic lesion. DES has been related to a higher risk of late stent thrombosis (LST) caused by delayed arterial healing. BRS theoretically leaves no pro-inflammatory substances or obstacles [4] and allows for restoration of vessel function [5]. This stent still has many challenges, such as appropriate stent design, mechanical property, biodegradation rate, and in vivo performance. Some studies have reported that BRS has a higher risk of device thrombosis than metallic stents [7,8,9,10,11,12,13], since thick stent struts are strongly correlated with blood-flow alterations and thrombogenicity. Extensive preclinical and clinical studies are needed to evaluate the efficacy and safety of stent designs

Methods

Results

Discussion

Conclusion