Nonlinear registration as an effective preprocessing technique for Deep learning based classification of disease

Abstract

A number of machine learning (ML), and particularly in recent years, deep learning (DL) approaches have been proposed for automatic classification of Alzheimer's disease (AD) using brain structural magnetic resonance imaging (MRI) data. However, the data available are limited in the case of this specific disease. Training a DL model with a large number of feature parameters on a small dataset of MRI scans will likely lead to overfitting. Overfitting reduces the generality and efficiency of the model. In this study, we show that a traditional nonlinear transformation from native space to template space, as a preprocessing stage, is effective in reducing overfitting through the reduction of spatial variations in the input data. To evaluate this effectiveness, we compare two different pre-processing approaches for DL-based AD classification task: (1) affine registration and (2) nonlinear diffeomorphic anatomical registration using exponentiated Lie algebra (DARTEL). The results show that the accuracy of the nonlinear registration based approach is much higher than the affine registration based approach. Furthermore, from the classification results obtained with noisy images, DARTEL is less susceptible to noise than affine registration. In summary, our experimental results suggest that nonlinear transformation is a preferable preprocessing step for training DL-based AD classification models on limited size datasets.