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Open Access

ARTICLE

TriLVM-UNet: Multi-Scale State Space Modeling with Cross-Channel Fusion Attention Mechanism for Precise Medical Image Segmentation

Kexin Zhang1, Lihua Liu1,*, Yuting Xue1, Tao Zhou2, Fengshuai Yue1, Ruifeng Du1
1 School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, China
2 School of Computer Science and Engineering, North Minzu University, Yinchuan, China
* Corresponding Author: Lihua Liu. Email: email
(This article belongs to the Special Issue: Development and Application of Deep Learning and Image Processing)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.082353

Received 14 March 2026; Accepted 08 June 2026; Published online 06 July 2026

Abstract

Traditional Mamba-UNet integrations employ four-stage architectures, replacing conventional five-stage UNets with VMamba blocks for global dependency modeling. Unlike Transformers, which suffer from quadratic complexity and high memory consumption in self-attention, Mamba-UNet achieves efficient global modeling through linear-complexity state space modeling. This paper proposes TriLVM-UNet, a lightweight three-stage architecture that integrates parameter-efficient VMamba blocks and enhances cross-stage feature interaction via an improved skip-attention bridge (SAB) module inspired by UltraLight VM-UNet. The model incorporates a Lightweight Vision Mamba (LVM) layer for high-resolution feature extraction, alongside multi-scale dilated convolution (MSDC) and convolutional block attention module (CBAM) for enhanced feature fusion. Evaluated on the 3D ACDC dataset against six baseline models, TriLVM-UNet achieves 98.57% accuracy. The GitHub repository is available at: https://github.com/730432ch/TriLVM-UNet.

Keywords

Medical image segmentation; visual state space model; TriLVM-UNet; lightweight architecture

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