  Brain age estimation from Magnetic Resonance Images (MRI) derives the
difference between a subject's biological brain age and their chronological
age. This is a potential biomarker for neurodegeneration, e.g. as part of
Alzheimer's disease. Early detection of neurodegeneration manifesting as a
higher brain age can potentially facilitate better medical care and planning
for affected individuals. Many studies have been proposed for the prediction of
chronological age from brain MRI using machine learning and specifically deep
learning techniques. Contrary to most studies, which use the whole brain
volume, in this study, we develop a new deep learning approach that uses 3D
patches of the brain as well as convolutional neural networks (CNNs) to develop
a localised brain age estimator. In this way, we can obtain a visualization of
the regions that play the most important role for estimating brain age, leading
to more anatomically driven and interpretable results, and thus confirming
relevant literature which suggests that the ventricles and the hippocampus are
the areas that are most informative. In addition, we leverage this knowledge in
order to improve the overall performance on the task of age estimation by
combining the results of different patches using an ensemble method, such as
averaging or linear regression. The network is trained on the UK Biobank
dataset and the method achieves state-of-the-art results with a Mean Absolute
Error of 2.46 years for purely regional estimates, and 2.13 years for an
ensemble of patches before bias correction, while 1.96 years after bias
correction.
