Segmentation of Edema in HGG MR Images Using Convolutional Neural Networks

  • S. Poornachandra
  • C. Naveena
  • Manjunath Aradhya
  • K. B. Nagasundara
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)


In this paper, we present the segmentation of edema subregion in the high-grade gliomas (HGGs) MR images We use the T1, T2, FLAIR, and T1c MRI modalities in our work and employ convolutional neural network approach (CNN) architecture for the segmentation task. Preprocessing was done in each case by correcting the inhomogeneities in MR images, equalizing histogram, and applying Z-score normalization to all the volumes. Here, we experimented with the convolutional layers, activation functions, and max-pooling layers. The CNN was trained on 3D patches extracted from patient volumes. Our experimentation has given promising results with mean dice score of 0.68, positive predicted value of 0.63, and sensitivity of 0.65 for edema segmentation.


Gliomas CNN MRI Dice score PPV and sensitivity 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • S. Poornachandra
    • 1
  • C. Naveena
    • 2
  • Manjunath Aradhya
    • 3
  • K. B. Nagasundara
    • 4
  1. 1.Department of Computer Science and ApplicationsVTU RRCBelgaumIndia
  2. 2.Department of Computer Science and EngineeringSJB Institute of TechnologyBengaluruIndia
  3. 3.Department of MCASri Jayachamarajendra College of EngineeringMysoreIndia
  4. 4.Department of Computer Science and EngineeringJSS Academy of Technical EducationBengaluruIndia

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