  Age is an important variable to describe the expected brain's anatomy status
across the normal aging trajectory. The deviation from that normative aging
trajectory may provide some insights into neurological diseases. In
neuroimaging, predicted brain age is widely used to analyze different diseases.
However, using only the brain age gap information (\ie the difference between
the chronological age and the estimated age) can be not enough informative for
disease classification problems. In this paper, we propose to extend the notion
of global brain age by estimating brain structure ages using structural
magnetic resonance imaging. To this end, an ensemble of deep learning models is
first used to estimate a 3D aging map (\ie voxel-wise age estimation). Then, a
3D segmentation mask is used to obtain the final brain structure ages. This
biomarker can be used in several situations. First, it enables to accurately
estimate the brain age for the purpose of anomaly detection at the population
level. In this situation, our approach outperforms several state-of-the-art
methods. Second, brain structure ages can be used to compute the deviation from
the normal aging process of each brain structure. This feature can be used in a
multi-disease classification task for an accurate differential diagnosis at the
subject level. Finally, the brain structure age deviations of individuals can
be visualized, providing some insights about brain abnormality and helping
clinicians in real medical contexts.

Computer Vision and Pattern Recognition