Most current medical image segmentation models are primarily built upon the U-shaped network (U-Net) architecture, which has certain limitations in capturing both global contextual information and fine-grained details. To address this issue, this paper proposes a novel U-shaped network model, termed the Multi-View U-Net (MUNet), which integrates self-attention and multi-view attention mechanisms. Specifically, a newly designed multi-view attention module is introduced to aggregate semantic features from different perspectives, thereby enhancing the representation of fine details in images. Additionally, the MUNet model leverages a self-attention encoding block to extract global image features, and by fusing global and local features, it improves segmentation performance. Experimental results demonstrate that the proposed model achieves superior segmentation performance in coronary artery image segmentation tasks, significantly outperforming existing models. By incorporating self-attention and multi-view attention mechanisms, this study provides a novel and efficient modeling approach for medical image segmentation, contributing to the advancement of intelligent medical image analysis.
当前医学图像分割模型大多以U型网络（U-Net）架构为基础进行构建，在捕捉图像全局信息与细节特征时存在一定局限性，为此本文设计了一种基于自注意力与多视角注意力的多视角U-Net网络（MUNet）模型，并提出了一种全新的多视角注意力模块，通过聚合来自不同视角的语义特征，增强图像的细节表达。同时，MUNet模型还可利用自注意力编码块提取图像的全局特征，通过全局与局部特征的融合提升模型的分割性能。实验结果表明，本文所提模型在冠状动脉血管图像分割任务中取得了优异的分割效果，显著优于现有模型。通过引入自注意力和多视角注意力机制，本文为医学图像分割领域提供了一种新的高效建模思路，有助于推动智能医学影像分析的发展。.
Keywords: Convolutional neural network; Medical image segmentation; Multi-view attention; Self-attention; Transformer.