A novel methodology for detection of lumen, outer wall, plaques and stent struts in coronary arteries using optical coherence tomography.

In this work, we present a novel and accurate methodology for the segmentation of optical coherence tomography imaging (OCT) and detection of lumen and outer wall, plaque characterization and stent struts in stented arteries. In particular, the methodology starts with pre-processing and detection of the catheter artefact. Struts detection is based on the identification of the size of the shadow behind the struts. Our methodology can be applied to metal stents as well as to polymeric and bioresorbable vascular scaffold (BVS) stents. Lumen segmentation is based on Fuzzy clustering and Fast marching on the gradient image to find the shortest path. The outer wall is segmented using a methodology, which combines K-means and 3-dimensional (3D) surface fitting on the detected edges. K-means with 3 clusters is performed at the final step on the ROI between the lumen and outer border of adventitia to characterize the plaque type. The validation is achieved by comparing the algorithm's results with manual annotations provided by experts. The results demonstrate that our methodology is accurate in lumen (R=0.99) and outer wall segmentation (R=0.77) and struts detection (R=0.82). The average Hausdorff distance and the Dice Similarity for lumen segmentation is 0.097 mm and 0.96, respectively.