Extraction of vascular wall in carotid ultrasound via a novel boundary-delineation network

Ultrasound imaging plays an essential role in the diagnosis of vascular lesions. Accurate segmentation of the vascular wall is important for preventing, diagnosing, and treating 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). First, we design the feature extraction module to prevent the feature information loss of low-quality images by multi-scale information fusion and multi-receptive field feature fusion. Secondly, we generate the initial coarse prediction results based on the extracted features. Based on the coarse prediction results, we use the boundary refinement module to obtain the boundary point locations and re-delineate the boundary points to prevent the boundary points from the offset. Finally, we use region mutual information loss and our designed global pixel relationship loss to model the relationship between pixels from global and neighbourhood aspects using the structural features of the vessel wall to help the model extract important structured information. 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.