Robust lumen segmentation in 3D contrast enhanced ultrasound images

The detection and quantification of carotid artery stenosis guides the decision of the need for surgical intervention such as endarterectomy or stenting of a patient. Contrast enhanced ultrasound highlights detailed information on hemodynam-ics and even on pathophysiology, and 3D scans yield full in-situ information of the complete anatomy. We introduce 3D image analysis algorithms that enable quantification and visualization of the carotid lumen in these scans. Our method enhances the lumen intensity contrast, extracts lumen centerlines using a gradient concentration calculation, and then uses a graph search method to obtain a coarse lumen segmentation, which is refined through a level set method applied on an intensity-corrected image. We processed 35 images acquired from 7 patients, and demonstrated quantitative comparisons with MR/CT lumen segmentations. We obtained a segmentation correlation score of R
²
= 0.94 and a coefficient of 0.99 for US versus MR/CT through a linear regression on effective diameters extracted from slices in the XY-plane.