Disentangling Hippocampal Shape Variations: A Study of Neurological
  Disorders Using Graph Variational Autoencoder with Contrastive Learning
Jakaria Rabbi Johannes Kiechle Christian Beaulieu Nilanjan Ray Dana Cobzas
This paper presents a comprehensive study focused on disentangling
hippocampal shape variations from diffusion tensor imaging (DTI) datasets
within the context of neurological disorders. Leveraging a Graph Variational
Autoencoder (VAE) enhanced with Supervised Contrastive Learning, our approach
aims to improve interpretability by disentangling two distinct latent variables
corresponding to age and the presence of diseases. In our ablation study, we
investigate a range of VAE architectures and contrastive loss functions,
showcasing the enhanced disentanglement capabilities of our approach. This
evaluation uses synthetic 3D torus mesh data and real 3D hippocampal mesh
datasets derived from the DTI hippocampal dataset. Our supervised
disentanglement model outperforms several state-of-the-art (SOTA) methods like
attribute and guided VAEs in terms of disentanglement scores. Our model
distinguishes between age groups and disease status in patients with Multiple
Sclerosis (MS) using the hippocampus data. Our Graph VAE with Supervised
Contrastive Learning shows the volume changes of the hippocampus of MS
populations at different ages, and the result is consistent with the current
neuroimaging literature. This research provides valuable insights into the
relationship between neurological disorder and hippocampal shape changes in
different age groups of MS populations using a Graph VAE with Supervised
Contrastive loss.