A Classification-Based Glioma Diffusion Model Using MRI Data

  • Marianne Morris
  • Russell Greiner
  • Jörg Sander
  • Albert Murtha
  • Mark Schmidt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4013)


Gliomas are diffuse, invasive brain tumors. We propose a 3D classification-based diffusion model, CDM, that predicts how a glioma will grow at a voxel-level, on the basis of features specific to the patient, properties of the tumor, and attributes of that voxel. We use Supervised Learning algorithms to learn this general model, by observing the growth patterns of gliomas from other patients. Our empirical results on clinical data demonstrate that our learned CDM model can, in most cases, predict glioma growth more effectively than two standard models: uniform radial growth across all tissue types, and another that assumes faster diffusion in white matter.


White Matter Grey Matter Glioma Cell Uniform Growth Edema Region 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marianne Morris
    • 1
  • Russell Greiner
    • 1
  • Jörg Sander
    • 1
  • Albert Murtha
    • 2
  • Mark Schmidt
    • 1
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  2. 2.Department of Radiation OncologyCross Cancer InstituteEdmontonCanada

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