Semi-automated carotid lumen segmentation in computedtomography angiography images

Carotid artery stenosis causes narrowing of carotid lumens and may lead to brain infarction. The purpose of thisstudy was to develop a semi-automated method of segmenting vessel walls, surrounding tissues, and moreimportantly, the carotid artery lumen by contrast computed tomography angiography (CTA) images and to define theseverity of stenosis and present a three-dimensional model of the carotid for visual inspection. In vivo contrast CTAimages of 14 patients (7 normal subjects and 7 patients undergoing endarterectomy) were analyzed using a multi-stepsegmentation algorithm. This method uses graph cut followed by watershed and Hessian based shortest path methodin order to extract lumen boundary correctly without being corrupted in the presence of surrounding tissues.Quantitative measurements of the proposed method were compared with those of manual delineation by independentboard-certified radiologists. The results were quantitatively evaluated using spatial overlap surface distance indices. Aslightly strong match was shown in terms of dice similarity coefficient (DSC) = 0.87_x005f
0.08; mean surface distance(Dmsd) = 0.32
0.32; root mean squared surface distance (Drmssd) = 0.49
0.54 and maximum surface distance (Dmax)= 2.14
2.08 between manual and automated segmentation of common, internal and external carotid arteries, carotidbifurcation and stenotic artery, respectively. Quantitative measurements showed that the proposed method has highpotential to segment the carotid lumen and is robust to the changes of the lumen diameter and the shape of the stenosisarea at the bifurcation site. The proposed method for CTA images provides a fast and reliable tool to quantify theseverity of carotid artery stenosis.