Summary of Disentangling Hippocampal Shape Variations: a Study Of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive Learning, by Jakaria Rabbi et al.
Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive Learning
by Jakaria Rabbi, Johannes Kiechle, Christian Beaulieu, Nilanjan Ray, Dana Cobzas
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive study on disentangling hippocampal shape variations from diffusion tensor imaging (DTI) datasets in the context of neurological disorders. It uses a Mesh Variational Autoencoder (VAE) enhanced with Supervised Contrastive Learning to improve interpretability by separating two distinct latent variables corresponding to age and disease presence. The approach is evaluated using synthetic 3D torus mesh data and real 3D hippocampal mesh datasets derived from the DTI hippocampal dataset. The results show that the supervised disentanglement model outperforms state-of-the-art methods like attribute and guided VAEs in terms of disentanglement scores, distinguishing between age groups and disease status in patients with Multiple Sclerosis (MS) using the hippocampus data. The study provides valuable insights into the relationship between neurological disorder and hippocampal shape changes in different age groups of MS populations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to understand how brain shape changes relate to diseases like multiple sclerosis. It uses special computer programs called mesh VAEs with contrastive learning to take 3D brain images from MRI machines. The program can separate the brain’s shape into two main parts: age and disease. This helps doctors see what’s happening in people’s brains at different ages and with different diseases. The study shows that this method works better than other methods, and it even matches what we already know about how multiple sclerosis affects the brain. |
Keywords
* Artificial intelligence * Diffusion * Supervised * Variational autoencoder
