  Carotid artery vessel wall thickness measurement is an essential step in the
monitoring of patients with atherosclerosis. This requires accurate
segmentation of the vessel wall, i.e., the region between an artery's lumen and
outer wall, in black-blood magnetic resonance (MR) images. Commonly used
convolutional neural networks (CNNs) for semantic segmentation are suboptimal
for this task as their use does not guarantee a contiguous ring-shaped
segmentation. Instead, in this work, we cast vessel wall segmentation as a
multi-task regression problem in a polar coordinate system. For each carotid
artery in each axial image slice, we aim to simultaneously find two
non-intersecting nested contours that together delineate the vessel wall. CNNs
applied to this problem enable an inductive bias that guarantees ring-shaped
vessel walls. Moreover, we identify a problem-specific training data
augmentation technique that substantially affects segmentation performance. We
apply our method to segmentation of the internal and external carotid artery
wall, and achieve top-ranking quantitative results in a public challenge, i.e.,
a median Dice similarity coefficient of 0.813 for the vessel wall and median
Hausdorff distances of 0.552 mm and 0.776 mm for lumen and outer wall,
respectively. Moreover, we show how the method improves over a conventional
semantic segmentation approach. These results show that it is feasible to
automatically obtain anatomically plausible segmentations of the carotid vessel
wall with high accuracy.
