A 3D Liver and Tumor Segmentation Method Based on U-Mamba and Efficient Paired Attention

Shili Yang, Xiaolong Zhang, Xiaoli Lin, He Deng, Hongwei Ren

Abstract: Accurate segmentation of the liver and tumor is crucial for clinical diagnosis. However, variations in liver size, similarity to adjacent organs, and the small, irregular nature of tumors pose significant challenges for automated methods. To tackle these problems, this paper proposes a 3D liver and tumor segmentation network named U-EPM, which is based on U-Mamba and efficient paired attention. In order to make full use of spatial information and establish global dependencies among features, this paper introduces efficient paired attention block into the U-Mamba block and names this combination the efficient paired attention Mamba module (EPM). In the EPM module, local details are first captured through convolution operations. The resulting features are then fed into the efficient paired attention block to further extract and fuse spatial and channel information. Finally, the Mamba block is applied to capture global contextual dependencies. During the decoder stage, a triple-path skip connection block (TSB) with branch weights is employed to fully leverage the information from the encoder, thus enhancing the segmentation performance of the model. Experimental results on the Abdomen CT, ATLAS, and private MRI datasets indicate that U-EPM demonstrates excellent performance in the tasks of liver and tumor segmentation.

External IDs:dblp:conf/icic/YangZLDR25