Embedded U-Net Liver Segmentation Model based on Multi-scale Semantic Information-MSN-Net Model

Abstract

In this paper, the problem of liver segmentation in liver CT images is studied, and the MSN-Net model is proposed. The single-hop connection in the traditional U-Net model is replaced by an embedded structure in the MSN-Net model. The embedded structure is a novel skip-connection architecture between the encoding path and the decoding path. The convolution operation is added to the structure to alleviate the semantic gap caused by the traditional skip connection; in addition, the high-dimensional-low-dimensional feature fusion strategy is used in the embedded structure, which combines the feature maps from high-dimensional and low-dimensional features. The feature maps are combined to enhance the feature expression ability of CT images and extract richer semantic information. The MSN-Net model is subjected to ablation experiments and compared with other current state-of-the-art methods. The experimental results show that the MSN-Net model proposed in this paper has superior segmentation performance.