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Related Experiment Video

Updated: Apr 16, 2026

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks
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COMMA: Coordinate-Aware Modulated Mamba Network for 3D Dispersed Vessel Segmentation.

Gen Shi, Hui Zhang, Jie Tian

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 14, 2026
    PubMed
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    We developed the Coordinate-aware Modulated Mamba Network (COMMA) for 3D medical image segmentation, improving spatial awareness in vascular structures. COMMA demonstrates superior performance, especially for small vessel segmentation, with enhanced computational efficiency.

    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Biomedical Engineering

    Background:

    • Accurate 3D segmentation of vascular structures is crucial for medical applications.
    • Current 3D segmentation models often lose spatial context due to patch-wise training.
    • Vascular structures' dispersed nature requires location awareness for precise segmentation.

    Purpose of the Study:

    • Introduce the Coordinate-aware Modulated Mamba Network (COMMA) for 3D vascular segmentation.
    • Address the loss of spatial context in existing segmentation models.
    • Provide the largest publicly available 3D cerebrovascular dataset (570 cases).

    Main Methods:

    • Developed COMMA, a network leveraging global and local branches for spatial awareness.
    • Utilized a channel-compressed Mamba (ccMamba) block for efficient encoding of full-resolution data and long-range dependencies.

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  • Proposed a coordinate-aware modulated (CaM) block to enhance global-local branch interaction.

    • Main Results:

    • COMMA achieved superior performance compared to state-of-the-art methods across six datasets (two modalities, five tissue types).
    • Demonstrated exceptional efficiency in segmenting small vessels.
    • Ablation studies confirmed the significance of proposed modules and spatial information integration.

    Conclusions:

    • COMMA effectively integrates spatial information for robust 3D vascular segmentation.
    • The proposed network offers computational efficiency and improved accuracy, particularly for challenging small vessel segmentation.
    • The new dataset significantly advances research in 3D cerebrovascular segmentation.