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Given the complex geometry of white matter streamlines, Autoencoders havebeen proposed as a dimension-reduction tool to simplify the analysisstreamlines in a low-dimensional latent spaces. However, despite these recentsuccesses, the majority of encoder architectures only perform dimensionreduction on single streamlines as opposed to a full bundle of streamlines.This is a severe limitation of the encoder architecture that completelydisregards the global geometric structure of streamlines at the expense ofindividual fibers. Moreover, the latent space may not be well structured whichleads to doubt into their interpretability. In this paper we propose a novelDifferentiable Vector Quantized Variational Autoencoder, which are engineeredto ingest entire bundles of streamlines as single data-point and providesreliable trustworthy encodings that can then be later used to analyzestreamlines in the latent space. Comparisons with several state of the artAutoencoders demonstrate superior performance in both encoding and synthesis.

Differentiable VQ-VAE's for Robust White Matter Streamline Encodings