Internal carotid artery stenosis is a major source of ischemic stroke. Multi-contrast MRI can be used for assessing wall characteristics and plaque progression. The quantification of vessel wall morphology requires an accurate segmentation of the vessel wall. To reduce inter- and intra-observer variability, we aim to provide a fully automatic segmentation method. Our approach for segmenting the lumen and vessel wall of the extracranial carotid arteries in T1-weighted 3D MR images is based on a 2D convolutional neural network. Average dice coefficients were 0.947/0.859 for the lumen/vessel wall and the median Hausdorff-distance was below the voxel-size of 0.6mm for both.