Work

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

Year: 2023

Type: conference-paper

Abstract: 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.

Source: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

DOI: 10.1109/embc40787.2023.10340009

Authors: Yixue Feng, Bramsh Q. Chandio, Sophia I. Thomopoulos, Tamoghna Chattopadhyay, Paul M. Thompson

Institution: University of Southern California

Language: English

Topic: Advanced Neuroimaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Institutes of Health, National Institute on Aging, HORIZON EUROPE Health