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Related Experiment Video

Updated: Apr 16, 2026

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks
10:53

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

Published on: January 3, 2017

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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
    Summary

    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.

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    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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    Last Updated: Apr 16, 2026

    Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks
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    Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

    Published on: January 3, 2017

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    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
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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.