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Related Experiment Video

Updated: Jan 16, 2026

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BrainSMM: Lifespan Brain Segmentation Model With Metadata-Driven Prompt Learning.

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    BrainSMM, a new metadata-driven model, accurately segments brain MRI across all ages by using text prompts. This approach improves consistency and detail preservation for neuroimaging applications.

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    Area of Science:

    • Neuroimaging
    • Medical Image Analysis
    • Artificial Intelligence in Medicine

    Background:

    • Accurate segmentation of brain MRI is vital for understanding brain development, aging, and diagnosing neurological diseases.
    • Current segmentation methods often perform inconsistently across different age groups (infants, adults).

    Purpose of the Study:

    • To introduce BrainSMM, a novel metadata-driven model for generalizable lifespan brain MRI segmentation.
    • To overcome the age-specific limitations of existing segmentation techniques.

    Main Methods:

    • BrainSMM utilizes text-based prompts, derived from prior knowledge (age, scanner, gender) via an image-text alignment model, to guide a segmentation backbone.
    • The model integrates these metadata prompts into a vision model to condition features for domain-specific contexts.

    Main Results:

    • Achieved 94.59% DSC for tissue segmentation (gray matter, white matter, CSF) and 86.34% for anatomical regions (hippocampus, putamen).
    • Demonstrated consistent accuracy across all age groups with improved anatomical detail preservation compared to baseline methods.
    • Showcased adaptability and transferability across multiple backbone architectures.

    Conclusions:

    • BrainSMM provides a robust and generalizable solution for lifespan brain MRI segmentation.
    • The metadata prompt technique enhances segmentation performance and consistency across diverse age groups.
    • This work supports enhanced clinical and developmental neuroimaging applications.