TY - EJOU AU - Zhang, Kexin AU - Liu, Lihua AU - Xue, Yuting AU - Zhou, Tao AU - Yue, Fengshuai AU - Du, Ruifeng TI - TriLVM-UNet: Multi-Scale State Space Modeling with Cross-Channel Fusion Attention Mechanism for Precise Medical Image Segmentation T2 - Computers, Materials \& Continua PY - VL - IS - SN - 1546-2226 AB - 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. KW - Medical image segmentation; visual state space model; TriLVM-UNet; lightweight architecture DO - 10.32604/cmc.2026.082353