Article Details

Title · Authors · Abstract · Keywords · DOI · References

Improved 3D Liver and Liver Tumor CT Image Segmentation Based on Mamba

GUO Jiahao, HU Huaifei

(South-Central Minzu University, Wuhan 430074, China)

Section：Computer Technology · Vol. 2026, Issue 3 · Pages：82-87 · 2096-4706（2026）03-0082-06 · CLC number：TP391.4 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2026.03.016

Abstract

Accurate segmentation of liver and liver tumors in three-dimensional Computed Tomography (CT) images is crucial for clinical diagnosis and treatment planning. To address the issues that existing Convolutional Neural Network (CNN) segmentation methods struggle to capture long-range dependencies due to limited receptive fields, and Transformer segmentation methods are restricted in application on three-dimensional medical images with limited samples, this paper proposes a new 3D medical image segmentation model based on Mamba. The model contains a 3D gated spatial convolution module and a channel shuffle upsampling module, which extract spatial correlation features and common features, respectively. The two modules synergistically enhance the modeling ability of the model for global structures, while simultaneously ensuring precise boundary positioning. Experimental results on the LiTS2017 dataset show that the Dice Similarity Coefficient (DSC) of liver segmentation reaches 96.42%, and the DSC of liver tumor segmentation reaches 70.70%. In the generalization experiment on the 3D-IRCADb dataset, the DSCs of liver and liver tumor segmentation reach 96.79% and 67.10%, respectively. Results of multiple comparative experiments further verify the superiority and robustness of the proposed model in segmentation performance.

Keywords

liver tumor segmentation; Mamba; State Space Model; Deep Learning

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About the authors

Guo Jiahao (1999-), male, Han ethnicity, from Taizhou, Zhejiang, is currently pursuing a master's degree in medical image processing and artificial intelligence.