Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRI

  • Sérgio PereiraEmail author
  • Adriano Pinto
  • Victor Alves
  • Carlos A. Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9556)


In their most aggressive form, the mortality rate of gliomas is high. Accurate segmentation is important for surgery and treatment planning, as well as for follow-up evaluation. In this paper, we propose to segment brain tumors using a Deep Convolutional Neural Network. Neural Networks are known to suffer from overfitting. To address it, we use Dropout, Leaky Rectifier Linear Units and small convolutional kernels. To segment the High Grade Gliomas and Low Grade Gliomas we trained two different architectures, one for each grade. Using the proposed method it was possible to obtain promising results in the 2015 Multimodal Brain Tumor Segmentation (BraTS) data set, as well as the second position in the on-site challenge.


Magnetic Resonance Imaging Brain tumor Glioma Segmentation Deep learning Deep Convolutional Neural Network 



This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI-01-0145-FEDER-006941. Sérgio Pereira was supported by a scholarship from Fundação para a Ciência e Tecnologia (FCT), Portugal (scholarship number PD/BD/105803/2014). Brain tumor image data used in this article were obtained from the MICCAI 2013 Challenge on Multimodal Brain Tumor Segmentation. The challenge database contain fully anonymized images from the Cancer Imaging Archive.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sérgio Pereira
    • 1
    • 2
    Email author
  • Adriano Pinto
    • 1
  • Victor Alves
    • 2
  • Carlos A. Silva
    • 1
  1. 1.CMEMS-UMinho Research UnitGuimarãesPortugal
  2. 2.Centro AlgoritmiUniversidade do MinhoBragaPortugal

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