Application of convolutional neural networks with anatomical knowledge for brain MRI analysis in MS patients

In this paper we consider the problem of automatic localization of multiple sclerosis (MS) lesions within brain tissue. We use a machine learning approach based on a convolutional neural network (CNN) which is trained to recognize the lesions in magnetic resonance images (MRI scans) of the patient's brain. The training images are relatively small fragments clipped from the MRI scans so - in order to provide additional hints on location of a given clip within the brain structures - we include anatomical information in the training/testing process. Our research has shown that indicating the location of the ventricles and other structures, as well as performing brain tissue classification may enhance the results of the automatic localization of the MS-related demyelinating plaques in the MRI scans.