  Quantitative analysis of vessel wall structures by automated vessel wall
segmentation provides useful imaging biomarkers in evaluating atherosclerotic
lesions and plaque progression time-efficiently. To quantify vessel wall
features, drawing lumen and outer wall contours of the artery of interest is
required. To alleviate manual labor in contour drawing, some computer-assisted
tools exist, but manual preprocessing steps, such as region of interest
identification and boundary initialization are needed. In addition, the prior
knowledge of the ring shape of vessel wall is not taken into consideration in
designing the segmentation method. In this work, trained on manual vessel wall
contours, a fully automated artery localization and vessel wall segmentation
system is proposed. A tracklet refinement algorithm is used to robustly
identify the centerlines of arteries of interest from a neural network
localization architecture. Image patches are extracted from the centerlines and
segmented in a polar coordinate system to use 3D information and to overcome
problems such as contour discontinuity and interference from neighboring
vessels. From a carotid artery dataset with 116 subjects (3406 slices) and a
popliteal artery dataset with 5 subjects (289 slices), the proposed system is
shown to robustly identify the artery of interest and segment the vessel wall.
The proposed system demonstrates better performance on the carotid dataset with
a Dice similarity coefficient of 0.824, compared with traditional vessel wall
segmentation methods, Dice of 0.576, and traditional convolutional neural
network approaches, Dice of 0.747. This vessel wall segmentation system will
facilitate research on atherosclerosis and assist radiologists in image review.
