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Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting.

Yixue Feng, Bramsh Q Chandio, Sophia I Thomopoulos

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    This study introduces a novel deep learning method to create synthetic brain white matter tract templates from limited data. These new templates better represent population-specific bundle shapes compared to traditional atlases.

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    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Machine Learning

    Background:

    • Standard atlases for white matter tract segmentation are time-consuming to create and may not represent diverse populations.
    • Existing methods struggle with the complexity and data-intensive nature of whole-brain tractography streamlines.

    Purpose of the Study:

    • To develop a deep generative model for creating synthetic, population-specific white matter bundle templates.
    • To improve the accuracy and applicability of automatic white matter tract segmentation.

    Main Methods:

    • Utilized a Convolutional Variational Autoencoder (CVAE) to map streamlines into a low-dimensional latent space.
    • Employed Kernel Density Estimation (KDE) on streamline embeddings to generate synthetic bundle templates.
    • Applied the framework to 50 subjects from the ADNI3 dataset.

    Main Results:

    • Generated synthetic population-specific bundle templates that better capture bundle shape distribution than standard atlases.
    • Quantitative shape analysis confirmed improved representation of bundle shapes.
    • Demonstrated successful direct bundle segmentation from whole-brain tractograms using the framework.

    Conclusions:

    • The deep generative model framework offers an efficient method for creating population-specific white matter templates.
    • This approach enhances the accuracy of white matter tract segmentation, particularly for diverse populations.
    • The method shows promise for advancing neuroimaging analysis and understanding brain connectivity.