标题： MSV-Mamba：一种用于超声心动图分割的多尺度视觉Mamba网络 显示英文标题

标题： MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentation

摘要： 超声成像经常遇到挑战，例如与高噪声水平、降低的时空分辨率以及解剖结构复杂性相关的问题。 这些因素显著阻碍了模型准确捕捉和分析心脏各个区域的结构关系和动态模式的能力。 Mamba是一种新兴的模型，是目前广泛应用于各种视觉和语言任务的最先进方法之一。 为此，本文引入了一种U型深度学习模型，结合了大窗口Mamba尺度（LMS）模块和分层特征融合方法用于超声心动图分割。 首先，级联残差块作为编码器，用于逐步提取多尺度的详细特征。 其次，将大窗口多尺度Mamba模块集成到解码器中，以捕捉跨区域的全局依赖关系，并增强对复杂解剖结构的分割能力。 此外，我们的模型在每个解码器层引入辅助损失，并采用双注意力机制来在空间和通道上融合多层特征。 这种方法提高了分割性能和精确度，特别是在描绘复杂解剖结构方面。 最后，使用EchoNet-Dynamic和CAMUS数据集的实验结果表明，该模型在准确性和鲁棒性方面优于其他方法。 对于左心室心内膜（${LV}_{endo}$）的分割，模型分别达到了95.01和93.36的最佳值，而对于左心室心外膜（${LV}_{epi}$），分别达到了87.35和87.80的值。 与表现最好的模型相比，这代表了0.54到1.11之间的改进。 显示英文摘要

摘要： Ultrasound imaging frequently encounters challenges, such as those related to elevated noise levels, diminished spatiotemporal resolution, and the complexity of anatomical structures. These factors significantly hinder the model's ability to accurately capture and analyze structural relationships and dynamic patterns across various regions of the heart. Mamba, an emerging model, is one of the most cutting-edge approaches that is widely applied to diverse vision and language tasks. To this end, this paper introduces a U-shaped deep learning model incorporating a large-window Mamba scale (LMS) module and a hierarchical feature fusion approach for echocardiographic segmentation. First, a cascaded residual block serves as an encoder and is employed to incrementally extract multiscale detailed features. Second, a large-window multiscale mamba module is integrated into the decoder to capture global dependencies across regions and enhance the segmentation capability for complex anatomical structures. Furthermore, our model introduces auxiliary losses at each decoder layer and employs a dual attention mechanism to fuse multilayer features both spatially and across channels. This approach enhances segmentation performance and accuracy in delineating complex anatomical structures. Finally, the experimental results using the EchoNet-Dynamic and CAMUS datasets demonstrate that the model outperforms other methods in terms of both accuracy and robustness. For the segmentation of the left ventricular endocardium (${LV}_{endo}$), the model achieved optimal values of 95.01 and 93.36, respectively, while for the left ventricular epicardium (${LV}_{epi}$), values of 87.35 and 87.80, respectively, were achieved. This represents an improvement ranging between 0.54 and 1.11 compared with the best-performing model.