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

Pauline Mouches, Matthias Wilms, Deepthi Rajashekar, Sonke Langner, Nils Forkert
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR 143:497-506, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v143-mouches21a, title = {Unifying Brain Age Prediction and Age-Conditioned Template Generation with a Deterministic Autoencoder}, author = {Mouches, Pauline and Wilms, Matthias and Rajashekar, Deepthi and Langner, Sonke and Forkert, Nils}, booktitle = {Proceedings of the Fourth Conference on Medical Imaging with Deep Learning}, pages = {497--506}, year = {2021}, editor = {Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schläfer, Alexander and Ernst, Floris}, volume = {143}, series = {Proceedings of Machine Learning Research}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v143/mouches21a/mouches21a.pdf}, url = {https://proceedings.mlr.press/v143/mouches21a.html}, 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.} }
Endnote
%0 Conference Paper %T Unifying Brain Age Prediction and Age-Conditioned Template Generation with a Deterministic Autoencoder %A Pauline Mouches %A Matthias Wilms %A Deepthi Rajashekar %A Sonke Langner %A Nils Forkert %B Proceedings of the Fourth Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2021 %E Mattias Heinrich %E Qi Dou %E Marleen de Bruijne %E Jan Lellmann %E Alexander Schläfer %E Floris Ernst %F pmlr-v143-mouches21a %I PMLR %P 497--506 %U https://proceedings.mlr.press/v143/mouches21a.html %V 143 %X 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.
APA
Mouches, P., Wilms, M., Rajashekar, D., Langner, S. & Forkert, N.. (2021). Unifying Brain Age Prediction and Age-Conditioned Template Generation with a Deterministic Autoencoder. Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 143:497-506 Available from https://proceedings.mlr.press/v143/mouches21a.html.

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