Glioma Segmentation and a Simple Accurate Model for Overall Survival Prediction

  • Evan Gates
  • J. Gregory Pauloski
  • Dawid Schellingerhout
  • David FuentesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)


Brain tumor segmentation is a challenging task necessary for quantitative tumor analysis and diagnosis. We apply a multi-scale convolutional neural network based on the DeepMedic to segment glioma subvolumes provided in the 2018 MICCAI Brain Tumor Segmentation Challenge. We go on to extract intensity and shape features from the images and cross-validate machine learning models to predict overall survival. Using only the mean FLAIR intensity, nonenhancing tumor volume, and patient age we are able to predict patient overall survival with reasonable accuracy.


Glioblastoma Segmentation Neural network Quantitative imaging 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Evan Gates
    • 1
  • J. Gregory Pauloski
    • 1
  • Dawid Schellingerhout
    • 2
  • David Fuentes
    • 1
    Email author
  1. 1.Department of Imaging PhysicsUniversity of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.Department of Cancer Systems Imaging and Diagnostic RadiologyUniversity of Texas MD Anderson Cancer CenterHoustonUSA

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