Detection of Midline Brain Abnormalities Using Convolutional Neural Networks

Patients with mental diseases have an increased prevalence of abnormalities in midline brain structures. One of these abnormalities is the cavum septum pellucidum (CSP), which occurs when the septum pellucidum fails to fuse. The detection and study of these brain abnormalities in Magnetic Resonance Imaging requires a tedious and time-consuming process of manual image analysis. It is also problematic when the same abnormality is analyzed manually by different experts because different criteria can be applied. In this context, it would be useful to develop an automatic method for locating the abnormality and give the measure of its depth. In this work, we explore, for the first time in the literature, an automated detection method based on CNNs. In particular, we compare different CNN models and classical machine learning classification algorithms to face this problem on a dataset of 861 subjects (639 patients with mood or psychotic disorders and 223 healthy controls) and obtain very promising results, reaching over 99% of accuracy, sensitivity and specificity.