@article{Zhang2026, 
author = {Kexin Zhang and Lihua Liu and Yuting Xue and Tao Zhou and Fengshuai Yue and Ruifeng Du},
title = {TriLVM-UNet: Multi-Scale State Space Modeling with Cross-Channel Fusion Attention Mechanism for Precise Medical Image Segmentation},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {3},
pages = {98},
keywords = {Medical image segmentation, visual state space model, TriLVM-UNet, lightweight architecture},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.082353},
doi = {10.32604/cmc.2026.082353},
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.}
}