Deep generative model for learning tractography streamline embeddings based on a Convolutional Variational Autoencoder
Yixue Feng1, Bramsh Qamar Chandio1,2, Tamoghna Chattopadhyay1, Sophia I. Thomopoulos1, Conor Owens-Walton1, Neda Jahanshad1, Eleftherios Garyfallidis2, and Paul M. Thompson1
1Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina Del Rey, CA, United States, 2Department of Intelligent Systems Engineering, School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States
We present a deep generative model to autoencode tractography streamlines into a smooth low dimensional latent distribution, which captures their spatial and sequential information with 1D convolutional layers. Using linear interpolation, we show that the learned latent space translates smoothly into the streamline space and can decode meaningful outputs from sampled points. This allows for inference on new data and direct use of Euclidean distance on the embeddings for downstream tasks, such as bundle labeling, quantitative inter-subject comparisons, and group statistics.
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