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Abstract #0850

Auto-encoded Latent Representations of White Matter Streamlines

Shenjun Zhong1, Zhaolin Chen1, and Gary Egan1,2
1Monash Biomedical Imaging, Monash University, Australia, Melbourne, Australia, 2School of Psychological Sciences, Monash University, Australia, Melbourne, Australia

Clustering white matter streamlines is still a challenging task. The existing methods based on spatial coordinates rely on manually engineered features, and/or labeled dataset. This work introduced a novel method that solves the problem of streamline clustering without needing labeled data. This is achieved by training a deep LSTM-based autoencoder to learn and embed any lengths of streamlines into a fixed-length vector, i.e. latent representation, then perform clustering in an unsupervised learning manner.

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