Semiautomatic segmentation and quantification of calcified plaques in intracoronary optical coherence tomography images

Abstract

Coronary calcified plaque (CP) is both an important marker of atherosclerosis and major determinant of the success of coronary stenting. Intracoronary optical coherence tomography (OCT) with high spatial resolution can provide detailed volumetric characterization of CP. We present a semiautomatic method for segmentation and quantification of CP in OCT images. Following segmentation of the lumen, guide wire, and arterial wall, the CP was localized by edge detection and traced using a combined intensity and gradient-based level-set model. From the segmentation regions, quantification of the depth, area, angle fill fraction, and thickness of the CP was demonstrated. Validation by comparing the automatic results to expert manual segmentation of 106 in vivo images from eight patients showed an accuracy of 78±9%. For a variety of CP measurements, the bias was insignificant (except for depth measurement) and the agreement was adequate when the CP has a clear outer border and no guide-wire overlap. These results suggest that the proposed method can be used for automated CP analysis in OCT, thereby facilitating our understanding of coronary artery calcification in the process of atherosclerosis and helping guide complex interventional strategies in coronary arteries with superficial calcification.