ERU-Net: An Enhanced Regression U-Net with Attention Gate and Adaptive Feature Fusion Block for Brain Age Prediction

The brain age is one of important biomarkers for identifying neurodegenerative diseases. Most existing works construct prediction models based on brain MRIs by deep learning methods while multi-scale features of MRI have been paid less attention. So, this paper proposes a new brain age predicting model, ERU-Net, which introduces an adaptive feature fusion (AFF) block into U-Net, for exploiting multi-scale features for high estimating accuracy. Moreover, the attention gate is also applied to enhance this model’s ability to extract important features among various brain regions. On a dataset of 6318 healthy people, ERU-Net achieves MAE = 2.85 and Pearson correlation coefficient R = 0.98, which outperforms other existing models for brain age estimation. In addition, by the results on the neurodegenerative disease datasets, it is clear that for the index of brain age gap (BAG), there are significantly statistical differences between healthy subjects and neurodegenerative patients such as Aizheimer’s disease, mild cognitive impairment, schizophrenia and Parkinson disease.