Semi-automatic 3D segmentation of carotid lumen in contrast-enhanced computed tomography angiography images

Abstract

The atherosclerosis disease is one of the major causes of the death in the world. Atherosclerosis refers to the hardening and narrowing of the arteries by plaques. Carotid stenosis is a narrowing or constriction of carotid artery lumen usually caused by atherosclerosis. Carotid artery stenosis can increase risk of brain stroke. Contrast-enhanced Computed Tomography Angiography (CTA) is a minimally invasive method for imaging and quantification of the carotid plaques. Manual segmentation of carotid lumen in CTA images is a tedious and time consuming procedure which is subjected to observer variability. As a result, there is a strong and growing demand for developing computer-aided carotid segmentation procedures. In this study, a novel method is presented for carotid artery lumen segmentation in CTA data. First, the mean shift smoothing is used for uniformity enhancement of gray levels. Then with the help of three seed points, the centerlines of the arteries are extracted by a 3D Hessian based fast marching shortest path algorithm. Finally, a 3D Level set function is performed for segmentation. Results on 14 CTA volumes data show 85% of Dice similarity and 0.42 mm of mean absolute surface distance measures. Evaluation shows that the proposed method requires minimal user intervention, low dependence to gray levels changes in artery path, resistance to extreme changes in carotid diameter and carotid branch locations. The proposed method has high accuracy and can be used in qualitative and quantitative evaluation.