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MambaDiff: Mamba-Enhanced Diffusion Model for 3D Medical Image Segmentation.

Yu Liu, Yan Feng, Juan Cheng

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 15, 2025
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    Summary

    This study introduces MambaDiff, a novel Mamba-enhanced diffusion model for 3D medical image segmentation. MambaDiff improves segmentation accuracy by enhancing feature extraction and preserving structural consistency, outperforming existing methods.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Accurate 3D medical image segmentation is vital for clinical diagnosis and treatment planning.
    • Diffusion models show potential in medical image segmentation but struggle with fine-grained feature preservation and structural consistency due to weak conditional guidance and limited feature extraction.
    • Existing methods often fail to capture intricate details and maintain spatial integrity in segmentation tasks.

    Purpose of the Study:

    • To develop an advanced diffusion model that overcomes the limitations of current approaches in 3D medical image segmentation.
    • To enhance the accuracy and structural consistency of medical image segmentation by improving feature extraction and conditional information integration.
    • To introduce a novel Mamba-enhanced diffusion model, MambaDiff, for superior performance in 3D medical image segmentation.

    Main Methods:

    • Proposed MambaDiff, a Mamba-enhanced diffusion model for 3D medical image segmentation.
    • Employed an encoder to extract multilevel semantic features from original images.
    • Integrated features using a Semantic Hierarchical Embedding (SHE) mechanism and incorporated a Global-Slice Perception Mamba (GSPM) layer for enhanced spatial reasoning and feature extraction.

    Main Results:

    • MambaDiff demonstrated competitive performance against state-of-the-art methods on four public medical image segmentation datasets (BraTS 2021, BraTS 2024, LiTS, MSD Hippocampus).
    • The proposed model achieved superior segmentation accuracy and structural consistency.
    • MambaDiff achieved these results with significantly fewer parameters compared to existing approaches.

    Conclusions:

    • MambaDiff effectively addresses the limitations of standard diffusion models in 3D medical image segmentation.
    • The integration of Mamba architecture and the proposed SHE and GSPM mechanisms significantly enhances feature extraction and preserves structural integrity.
    • The model offers a more parameter-efficient and accurate solution for critical medical image segmentation tasks.