DeepMedic for Brain Tumor Segmentation

  • Konstantinos KamnitsasEmail author
  • Enzo Ferrante
  • Sarah Parisot
  • Christian Ledig
  • Aditya V. Nori
  • Antonio Criminisi
  • Daniel Rueckert
  • Ben Glocker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)


Accurate automatic algorithms for the segmentation of brain tumours have the potential of improving disease diagnosis, treatment planning, as well as enabling large-scale studies of the pathology. In this work we employ DeepMedic [1], a 3D CNN architecture previously presented for lesion segmentation, which we further improve by adding residual connections. We also present a series of experiments on the BRATS 2015 training database for evaluating the robustness of the network when less training data are available or less filters are used, aiming to shed some light on requirements for employing such a system. Our method was further benchmarked on the BRATS 2016 Challenge, where it achieved very good performance despite the simplicity of the pipeline.


Convolutional Neural Network Conditional Random Field Training Database Segmentation Task Tumour Segmentation 
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.



This work is supported by the EPSRC (grant No: EP/N023668/1) and partially funded under the 7th Framework Programme by the European Commission (TBIcare:; CENTER-TBI: Part of this work was carried on when KK was an intern at Microsoft Research Cambridge. KK is also supported by the President’s PhD Scholarship of Imperial College London. We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs for our research.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Konstantinos Kamnitsas
    • 1
    • 2
    Email author
  • Enzo Ferrante
    • 1
  • Sarah Parisot
    • 1
  • Christian Ledig
    • 1
  • Aditya V. Nori
    • 2
  • Antonio Criminisi
    • 2
  • Daniel Rueckert
    • 1
  • Ben Glocker
    • 1
  1. 1.Biomedical Image Analysis GroupImperial College LondonLondonUK
  2. 2.Microsoft ResearchCambridgeUK

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