  Segmentation of carotid vessel wall is required in vessel wall volume (VWV)
and local vessel-wall-plus-plaque thickness (VWT) quantification of the carotid
artery. Manual segmentation of the vessel wall is time-consuming and prone to
interobserver variability. In this paper, we proposed a convolution neural
network to segment the common carotid artery (CCA) from 3D carotid ultrasound
images. The proposed CNN involves three U-Nets that segmented the 3D ultrasound
(3DUS) images in the axial, lateral and frontal orientations. The segmentation
maps generated by three U-Nets were consolidated by a novel segmentation
average network (SAN) we proposed in this paper. The experimental results show
that the proposed CNN improved the Dice similarity coefficient (DSC) for vessel
wall segmentation from 64.8% to 67.5%, the sensitivity from 63.8% to 70.5%, and
the area under receiver operator characteristic curve (AUC) from 0.89 to 0.94.
