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Enhanced Automatic Segmentation for Superficial White Matter Fiber Bundles for Probabilistic Tractography Datasets.

C Mendoza, C Roman, A Vazquez

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    This study introduces a novel three-step algorithm for precise automatic segmentation of superficial white matter (SWM) bundles using diffusion MRI tractography. The enhanced method improves fiber identification and accuracy compared to existing techniques.

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    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Medical Image Analysis

    Background:

    • Superficial white matter (SWM) bundle segmentation is crucial for understanding brain connectivity and pathologies.
    • Existing segmentation methods using Euclidean distance can include noisy fibers, affecting accuracy.
    • A robust and accurate method for SWM bundle segmentation is needed for clinical research applications.

    Purpose of the Study:

    • To develop and validate an enhanced algorithm for automatic segmentation of SWM bundles from diffusion MRI tractography data.
    • To improve the accuracy and reliability of SWM bundle identification by effectively discarding noisy fibers.
    • To enhance the clinical relevance of SWM segmentation for studying brain disorders.

    Main Methods:

    • A three-step algorithm incorporating fiber clustering, Symmetrized Segment-Path Distance (SSPD) filtering in 2D and 3D, and outlier removal.
    • Segmentation performed between cluster centroids and atlas centroids to remove outliers and identify similar fiber shapes.
    • Experimental evaluation on ten Human Connectome Project (HCP) subjects, comparing results to manual segmentations of bundles connecting precentral and postcentral gyri.

    Main Results:

    • The proposed method successfully discards noisy fibers, leading to improved identification of SWM bundles.
    • Bundles segmented using the enhanced algorithm demonstrated higher similarity scores compared to the state-of-the-art method.
    • The approach maintained a comparable number of fibers to manual segmentations, indicating efficiency.

    Conclusions:

    • The enhanced algorithm provides a more accurate and reliable method for automatic SWM bundle segmentation.
    • Improved SWM segmentation facilitates advancements in clinical research for various brain pathologies.
    • This technique offers a valuable tool for neuroimaging analysis and understanding white matter organization.