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

White matter tracts generated from whole brain tractography are often processed using automatic segmentation methods with standard atlases. Atlases are generated from hundreds of subjects, which becomes time-consuming to create and difficult to apply to all populations. In this study, we extended ou...

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Veröffentlicht in:bioRxiv
Hauptverfasser: Feng, Yixue, Chandio, Bramsh Q, Thomopoulos, Sophia I, Chattopadhyay, Tamoghna, Thompson, Paul M
Format: Journal Article Paper
Sprache:Englisch
Veröffentlicht: United States Cold Spring Harbor Laboratory Press 09.05.2023
Cold Spring Harbor Laboratory
Ausgabe:1.3
Schlagworte:
Neuroscience
Population studies
Segmentation
Substantia alba
ISSN:2692-8205, 2692-8205
Online-Zugang:Volltext
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Zusammenfassung:White matter tracts generated from whole brain tractography are often processed using automatic segmentation methods with standard atlases. Atlases are generated from hundreds of subjects, which becomes time-consuming to create and difficult to apply to all populations. In this study, we extended our prior work on using a deep generative model a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space given a limited sample size of 50 subjects from the ADNI3 dataset, to generate synthetic population-specific bundle templates using Kernel Density Estimation (KDE) on streamline embeddings. We conducted a quantitative shape analysis by calculating bundle shape metrics, and found that our bundle templates better capture the shape distribution of the bundles than the atlas data used in the original segmentation derived from young healthy adults. We further demonstrated the use of our framework for direct bundle segmentation from whole-brain tractograms.
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Competing Interest Statement: The authors have declared no competing interest.
ISSN:2692-8205
2692-8205
DOI:10.1101/2023.02.24.529954