  Semantic segmentation is an established while rapidly evolving field in
medical imaging. In this paper we focus on the segmentation of brain Magnetic
Resonance Images (MRI) into cerebral structures using convolutional neural
networks (CNN). CNNs achieve good performance by finding effective high
dimensional image features describing the patch content only. In this work, we
propose different ways to introduce spatial constraints into the network to
further reduce prediction inconsistencies.
  A patch based CNN architecture was trained, making use of multiple scales to
gather contextual information. Spatial constraints were introduced within the
CNN through a distance to landmarks feature or through the integration of a
probability atlas. We demonstrate experimentally that using spatial information
helps to reduce segmentation inconsistencies.
