  Carotid vessel wall segmentation is a crucial yet challenging task in the
computer-aided diagnosis of atherosclerosis. Although numerous deep learning
models have achieved remarkable success in many medical image segmentation
tasks, accurate segmentation of carotid vessel wall on magnetic resonance (MR)
images remains challenging, due to limited annotations and heterogeneous
arteries. In this paper, we propose a semi-supervised label propagation
framework to segment lumen, normal vessel walls, and atherosclerotic vessel
wall on 3D MR images. By interpolating the provided annotations, we get 3D
continuous labels for training 3D segmentation model. With the trained model,
we generate pseudo labels for unlabeled slices to incorporate them for model
training. Then we use the whole MR scans and the propagated labels to re-train
the segmentation model and improve its robustness. We evaluated the label
propagation framework on the CarOtid vessel wall SegMentation and
atherosclerOsis diagnosiS (COSMOS) Challenge dataset and achieved a QuanM score
of 83.41\% on the testing dataset, which got the 1-st place on the online
evaluation leaderboard. The results demonstrate the effectiveness of the
proposed framework.
