Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors

• URL: http://arxiv.org/abs/2211.07820v2
• Date: Wed, 16 Nov 2022 08:07:04 GMT
• Title: Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors
• Authors: Anjun Hu, Jean-Pierre R. Falet, Brennan S. Nichyporuk, Changjian Shui, Douglas L. Arnold, Sotirios A. Tsaftaris, Tal Arbel
• Abstract summary: We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI.
• Score: 11.74918328561702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain lesions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI to ensure the clinical validity of generated samples; (2) preserves and disentangles finer pathological details pertaining to a patient's disease state. Additionally, we experiment with an alternative training configuration where we provide supervision to a subset of latent units. It is shown that (1) a partially supervised latent space achieves a higher degree of disentanglement between evidence of disease and subject-specific anatomy; (2) when the prior is formulated with an autoregressive structure, knowledge from the supervision can propagate to the unsupervised latent units, resulting in more informative latent representations capable of modelling anatomy-pathology interdependencies.

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