Fully Automated Ultrasound Common Carotid Artery Segmentation Using Active Shape Model

Abstract

B-mode measurement of the contour of the common carotid artery (CCA) has an important clinical value. The purpose of this study was to develop a fully automated ultrasound common carotid artery segmentation method using active shape model (ASM). An image database with 90 images was used to train the ASM model during the offline training phase of ASM. When it came to the online segmentation phase, a knowledge-based seed point detection method was first used to locate the centroid of the CCA. Then the trained ASM model automatically produced an exact contour of the CCA. The proposed method yielded a Dice Metric of 90.5% ± 4.35% and a Hausdorff Distance of 9.28 ± 5.2 pixels in a database of 40 ultrasound images. The segmentation result of upper and bottom part of the CCA was better than that of lateral part of the CCA. The proposed method eliminates the need of manual initialization, and identifies the contour of the CCA with high precision. It has the potential to be a suitable replacement for manual segmentation of the CCA.