Deep neural networks for anatomical brain segmentation

Alexandre De Brébisson, Giovanni Montana

Research output: Contribution to journalConference paperpeer-review

237 Citations (Scopus)


We present a novel approach to automatically segment magnetic resonance (MR) images of the human brain into anatomical regions. Our methodology is based on a deep artificial neural network that assigns each voxel in an MR image of the brain to its corresponding anatomical region. The inputs of the network capture information at different scales around the voxel of interest: 3D and orthogonal 2D intensity patches capture a local spatial context while downscaled large 2D orthogonal patches and distances to the regional centroids enforce global spatial consistency. Contrary to commonly used segmentation methods, our technique does not require any non-linear registration of the MR images. To benchmark our model, we used the dataset provided for the MICCAI 2012 challenge on multi-atlas labelling, which consists of 35 manually segmented MR images of the brain. We obtained competitive results (mean dice coefficient 0.725, error rate 0.163) showing the potential of our approach. To our knowledge, our technique is the first to tackle the anatomical segmentation of the whole brain using deep neural networks.

Original languageEnglish
Pages (from-to)20-28
Number of pages9
JournalIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Publication statusPublished - 19 Oct 2015
EventIEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015 - Boston, United States
Duration: 7 Jun 201512 Jun 2015


  • Biological neural networks
  • Feature extraction
  • Image segmentation
  • Magnetic resonance imaging
  • Neurons
  • Three-dimensional displays
  • Training


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