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.

翻译：年龄是描述正常衰老轨迹中预期大脑解剖状态的重要变量。与正常衰老轨迹的偏差可为神经系统疾病提供重要线索。在神经影像学中，预测脑年龄被广泛用于分析不同疾病。然而，仅利用脑年龄差距信息（即实际年龄与估计年龄之差）对疾病分类问题的信息量可能不足。本文提出通过结构磁共振成像估计脑结构年龄来扩展全局脑年龄的概念。为此，首先采用深度集成学习模型估计三维衰老图谱（即体素级年龄估计），继而利用三维分割掩膜获取最终脑结构年龄。该生物标志物可在多种场景中应用：首先，可实现群体水平的异常检测，准确估计脑年龄——在此场景下，本方法优于多种现有最优方法；其次，可通过脑结构年龄计算各脑结构与正常衰老过程的偏离程度，该特征可应用于个体水平的多疾病分类任务，实现精准鉴别诊断；最后，可可视化个体的脑结构年龄偏差，为脑部异常提供重要信息，辅助临床医生在真实医疗场景中决策。