MRI Image Registration Considerably Improves CNN-Based Disease Classification

Machine learning methods have many promising applications in medical imaging, including the diagnosis of Alzheimer's Disease (AD) based on magnetic resonance imaging (MRI) brain scans. These scans usually undergo several preprocessing steps, including image registration. However, the effect of image registration methods on the performance of the machine learning classifier is poorly understood. In this study, we train a convolutional neural network (CNN) to detect AD on a dataset preprocessed in three different ways. The scans were registered to a template either linearly or nonlinearly, or were only padded and cropped to the needed size without performing image registration. We show that both linear and nonlinear registration increase the balanced accuracy of the classifier significantly by around 6-7% in comparison to no registration. No significant difference between linear and nonlinear registration was found. The dataset split, although carefully matched for age and sex, affects the classifier performance strongly, suggesting that some subjects are easier to classify than others, possibly due to different clinical manifestations of AD and varying rates of disease progression. In conclusion, we show that for a CNN detecting AD, a prior image registration improves the classifier performance, but the choice of a linear or nonlinear registration method has only little impact on the classification accuracy and can be made based on other constraints such as computational resources or planned further analyses like the use of brain atlases.