@Article{cmc.2026.082353,
AUTHOR = {Kexin Zhang, Lihua Liu, Yuting Xue, Tao Zhou, Fengshuai Yue, Ruifeng Du},
TITLE = {TriLVM-UNet: Multi-Scale State Space Modeling with Cross-Channel Fusion Attention Mechanism for Precise Medical Image Segmentation},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27430},
ISSN = {1546-2226},
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: <a href="https://github.com/730432ch/TriLVM-UNet" target="_blank">https://github.com/730432ch/TriLVM-UNet</a>.},
DOI = {10.32604/cmc.2026.082353}
}