Extraction of Vascular Wall in Carotid Ultrasound via a Novel Boundary-Delineation Network
This work addresses segmentation errors in vascular wall boundaries for clinical ultrasound diagnosis, representing an incremental improvement with specific technical enhancements.
The paper tackles inaccurate localization of vascular wall boundaries in carotid ultrasound segmentation by proposing a novel boundary-delineation network (BDNet), which achieves the best segmentation results and significantly reduces memory consumption compared to existing models.
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.