A Convolutional Neural Network Approach to Brain Tumor Segmentation

  • Mohammad HavaeiEmail author
  • Francis Dutil
  • Chris Pal
  • Hugo Larochelle
  • Pierre-Marc Jodoin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9556)


We consider the problem of fully automatic brain focal pathology segmentation, in MR images containing low and high grade gliomas and ischemic stroke lesion. We propose a Convolutional Neural Network (CNN) approach which is amongst the top performing methods while also being extremely computationally efficient, a balance that existing methods have struggled to achieve. Our CNN is trained directly on the image modalities and thus learns a feature representation directly from the data. We propose a cascaded architecture with two pathways: one which focuses on small details in gliomas and one on the larger context. We also propose a two-phase patch-wise training procedure allowing us to train models in a few hours. Fully exploiting the convolutional nature of our model also allows us to segment a complete brain image in 25 s to 3 min. Experimental results on BRain Tumor Segmentation challenges (BRATS’13, BRATS’15) and Ischemic Stroke Lesion Segmentation challenge (ISLES’15) reveal that our approach is among the most accurate in the literature, while also being computationally very efficient.


Convolutional Neural Network Stochastic Gradient Descent Challenge Data Challenge Dataset Deep Convolutional Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mohammad Havaei
    • 1
    Email author
  • Francis Dutil
    • 1
  • Chris Pal
    • 2
  • Hugo Larochelle
    • 1
  • Pierre-Marc Jodoin
    • 1
  1. 1.Université de SherbrookeSherbrookeCanada
  2. 2.École Polytechnique de MontréalMontréalCanada

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