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A fast atlas pre-selection procedure for multi-atlas based brain segmentation.

Jingbo Ma, Heather T Ma, Hengtong Li

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary

    A new method speeds up brain MR image segmentation by 20x using lateral ventricle (LV) similarity for atlas pre-selection. This approach maintains segmentation accuracy while significantly reducing computational load for quantitative brain analysis.

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    Area of Science:

    • Neuroimaging
    • Medical Image Analysis
    • Computational Anatomy

    Background:

    • Multi-atlas based segmentation is crucial for quantitative brain MR image analysis.
    • Increasing atlas databases necessitate efficient atlas pre-selection methods.
    • Automated segmentation requires accurate and fast atlas selection.

    Purpose of the Study:

    • To propose and evaluate a novel atlas pre-selection method for multi-atlas based MR image segmentation.
    • To improve the computational efficiency of the atlas pre-selection step in the MriCloud platform.
    • To assess the accuracy of the proposed method against existing techniques.

    Main Methods:

    • Developed an atlas pre-selection strategy based on lateral ventricle (LV) label similarity.
    • Utilized Dice overlap coefficient as the quantitative measure for atlas ranking.
    • Compared the proposed LV similarity method with mutual information (MI) based pre-selection.

    Main Results:

    • The proposed LV similarity method achieved comparable segmentation accuracy to MI-based pre-selection.
    • The computational load for atlas pre-selection was reduced by approximately 20 times.
    • The method demonstrated significant speed-up without compromising accuracy.

    Conclusions:

    • The LV similarity based atlas pre-selection offers a computationally efficient alternative for brain MR image segmentation.
    • This method enhances the practicality of multi-atlas segmentation on platforms like MriCloud.
    • The approach shows promise for accelerating quantitative analysis of brain images.