Auto-encoded Latent Representations of White Matter Streamlines for Quantitative Distance Analysis

Abstract

Parcellation of whole brain tractograms is a critical step to study brain white matter structures and connectivity patterns. The existing methods based on supervised classification of streamlines into predefined streamline bundle types are not designed to explore sub-bundle structures, and methods with manually designed features are expensive to compute streamline-wise similarities. To resolve these issues, we propose a novel atlas-free method that learns a latent space using a deep recurrent auto-encoder trained in an unsupervised manner. The method efficiently embeds any length of streamlines to fixed-size feature vectors, named streamline embedding, for tractogram parcellation using non-parametric clustering in the latent space. The method was evaluated on the ISMRM 2015 tractography challenge dataset with discrimination of major bundles using clustering algorithms and streamline querying based on similarity, as well as real tractograms of 102 subjects Human Connectome Project. The learnt latent streamline and bundle representations open the possibility of quantitative studies of arbitrary granularity of sub-bundle structures using generic data mining techniques.

Learning optimal white matter tract representations from tractography using a deep generative model for population analyses

“…9 Zhong et al, for example, encoded streamlines with a recurrent autoencoder and used the embeddings for bundle parcellation. 10 A similar approach, using a convolutional autoencoder, was developed for tractogram filtering. 4 While these studies show that the learned embeddings can retain bundle information such as their shapes and positions, the latent space for standard autoencoders is not continuous and the model is often prone to overfitting, 11 making it difficult to evaluate embeddings on unseen data, and use them for population analyses which involve large amounts of data.…”