Fully automated lumen segmentation of intracoronary optical coherence tomography images

Abstract

Optical coherence tomography (OCT) provides high-resolution cross-sectional images of arterial luminal morphology. Traditionally lumen segmentation of OCT images is performed manually by expert observers; a laborious, time consuming effort, sensitive to inter-observer variability process. Although several automated methods have been developed, the majority cannot be applied in real time because of processing demands. To address these limitations we propose a new method for rapid image segmentation of arterial lumen borders using OCT images that involves the following steps: 1) OCT image acquisition using the raw OCT data, 2) reconstruction of longitudinal cross-section (LOCS) images from four different acquisition angles, 3) segmentation of the LOCS images and 4) lumen contour construction in each 2D cross-sectional image. The efficiency of the developed method was evaluated using 613 annotated images from 10 OCT pullbacks acquired from 10 patients at the time of coronary arterial interventions. High Pearson's correlation coefficient was obtained when lumen areas detected by the method were compared to areas annotated by experts (r=0.98, R 2 =0.96); Bland-Altman analysis showed no significant bias with good limits of agreement. The proposed methodology permits reliable border detection especially in lumen areas having artifacts and is faster than traditional techniques making it capable of being used in real time applications. The method is likely to assist in a number of research and clinical applications - further testing in an expanded clinical arena will more fully define the limits and potential of this approach.