Unifying Brain Age Prediction and Age-Conditioned Template Generation with a Deterministic Autoencoder

Pauline Mouches, Matthias Wilms, Deepthi Rajashekar, Sonke Langner, Nils Forkert

Keywords: Brain aging, Deep learning, Generative models

TL;DR: This paper proposes a novel deep learning based approach to unify biological brain age estimation and age-conditioned brain template creation in a single and consistent model.

Abstract: Age-related morphological brain changes are known to be different in healthy and disease-affected aging. Biological brain age estimation from MRI scans is a common way to quantify this effect whereas differences between biological and chronological age indicate degenerative processes. The ability to visualize and analyze the morphological age-related changes in the image space directly is essential to improve the understanding of brain aging. In this work, we propose a novel deep learning based approach to unify biological brain age estimation and age-conditioned template creation in a single, consistent model. We achieve this by developing a deterministic autoencoder that successfully disentangles age-related morphological changes and subject-specific variations. This allows its use as a brain age regressor as well as a generative brain aging model. The proposed approach demonstrates accurate biological brain age prediction, and realistic generation of age-conditioned brain templates and simulated age-specific brain images when applied to a database of more than 2000 subjects.

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Source Code Url: https://github.com/pmouches/Brain-Age-Prediction-and-Age-Conditioned-Template-Generation

Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.

Data Set Url: https://brain-development.org/ixi-dataset/

Paper Type: both

Primary Subject Area: Image Synthesis

Secondary Subject Area: Detection and Diagnosis