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LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation

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arxiv 2403.05246 v2 pith:MYJ54QNW submitted 2024-03-08 eess.IV cs.CV

Weibin Liao , Yinghao Zhu , Xinyuan Wang , Chengwei Pan , Yasha Wang , Liantao Ma This is my paper · ORCID
classification eess.IVcs.CV
keywords mambalightm-unetunetlightweightcomputationalmedicalsegmentationtransformer
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UNet and its variants have been widely used in medical image segmentation. However, these models, especially those based on Transformer architectures, pose challenges due to their large number of parameters and computational loads, making them unsuitable for mobile health applications. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as competitive alternatives to CNN and Transformer architectures. Building upon this, we employ Mamba as a lightweight substitute for CNN and Transformer within UNet, aiming at tackling challenges stemming from computational resource limitations in real medical settings. To this end, we introduce the Lightweight Mamba UNet (LightM-UNet) that integrates Mamba and UNet in a lightweight framework. Specifically, LightM-UNet leverages the Residual Vision Mamba Layer in a pure Mamba fashion to extract deep semantic features and model long-range spatial dependencies, with linear computational complexity. Extensive experiments conducted on two real-world 2D/3D datasets demonstrate that LightM-UNet surpasses existing state-of-the-art literature. Notably, when compared to the renowned nnU-Net, LightM-UNet achieves superior segmentation performance while drastically reducing parameter and computation costs by 116x and 21x, respectively. This highlights the potential of Mamba in facilitating model lightweighting. Our code implementation is publicly available at https://github.com/MrBlankness/LightM-UNet.

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Cited by 8 Pith papers

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    WMF, the energy-weighted mean frequency of the temporal Fourier transform of 4D Flow MRI velocity, separates pulsatile flow from background better than PC-MRA and improves aorta segmentation.

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    eess.IV 2025-06 conditional novelty 5.0 of 10

    Pretrained pure VRWKV encoders paired with pure VRWKV decoders match or beat CNN, ViT, and Mamba baselines, with a small model plus FAWA and MSCF modules reaching 88% average Dice.

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    On the public ARCADE coronary stenosis segmentation benchmark, the U-Mamba BOT model achieves an F1 score of 68.79%, the highest reported to date.

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