  Ultrasound imaging plays an important role in the diagnosis of vascular
lesions. Accurate segmentation of the vascular wall is important for the
prevention, diagnosis and treatment of vascular diseases. However, existing
methods have inaccurate localization of the vascular wall boundary.
Segmentation errors occur in discontinuous vascular wall boundaries and dark
boundaries. To overcome these problems, we propose a new boundary-delineation
network (BDNet). We use the boundary refinement module to re-delineate the
boundary of the vascular wall to obtain the correct boundary location. We
designed the feature extraction module to extract and fuse multi-scale features
and different receptive field features to solve the problem of dark boundaries
and discontinuous boundaries. We use a new loss function to optimize the model.
The interference of class imbalance on model optimization is prevented to
obtain finer and smoother boundaries. Finally, to facilitate clinical
applications, we design the model to be lightweight. Experimental results show
that our model achieves the best segmentation results and significantly reduces
memory consumption compared to existing models for the dataset.
