Multiple sclerosis is a chronic inflammatory disease characterized by demyelination. Magnetic resonance imaging (MRI) is an important method for diagnosis and prognosis predictions. The ongoing study presented here shows the use of deep learning algorithms for white and grey matter lesion segmentation in 7T MRI images. Results show high accuracy for patients with high lesion load. Furthermore, it is demonstrated that it is possible to train a neural net to find small cortical lesions, which can be used as a potential biomarker.