Towards Integrating Spatial Localization in Convolutional Neural Networks for Brain Image Segmentation

Pierre-Antoine Ganaye (1) , Michael Sdika (2, 3) , Hugues Benoit-Cattin (2)

1 CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
2 Images et Modèles
3 Service Informatique et développements

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.

Mots clés: Brain MRI segmentation; probability atlas; CNN; spatial context; landmarks

Domaines: Intelligence artificielle [cs.AI]; Médecine humaine et pathologie

Pierre-Antoine Ganaye, Michael Sdika, Hugues Benoit-Cattin. Towards Integrating Spatial Localization in Convolutional Neural Networks for Brain Image Segmentation. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Apr 2018, Washington, United States. ⟨10.1109/ISBI.2018.8363652⟩. ⟨hal-01812045⟩