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.
翻译:超声成像在血管损伤诊断中起着重要作用。血管墙的准确分解对于血管疾病的预防、诊断和治疗十分重要。但是,现有方法对血管墙边界的定位不准确。分解错误发生在不连续的血管墙边界和黑暗边界中。为了克服这些问题,我们提议建立一个新的边界分解网络(BDNet)。我们使用边界精细化模块重新分解血管墙的边界以获得正确的边界位置。我们设计了地貌提取模块,以提取和结合多尺寸特征和不同的可接受场域特征,解决暗界和不连续边界的问题。我们使用新的损失功能优化模型。模型优化时防止阶级不平衡的干扰,以获得细微和更平滑的边界。最后,为了便利临床应用,我们设计了一种轻度的模型。实验结果表明,我们的模型取得了最佳分解结果,大大降低了与现有数据集模型相比的记忆消耗量。