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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