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:2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Jg. 2023; S. 1 - 6
Hauptverfasser: Feng, Yixue, Chandio, Bramsh Q., Thomopoulos, Sophia I., Chattopadhyay, Tamoghna, Thompson, Paul M.
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2023
Schlagworte:
Adult
Benchmarking
Brain - diagnostic imaging
Estimation
Humans
Image Processing, Computer-Assisted - methods
Kernel
Measurement
Shape
Sociology
Training
White matter
White Matter - diagnostic imaging
ISSN:2694-0604, 2694-0604
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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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ISSN:2694-0604
2694-0604
DOI:10.1109/EMBC40787.2023.10340009