A classification-based glioma diffusion model using MRI data

Authors:
Marianne Morris;Russell Greiner;Jörg Sander;Albert Murtha;Mark Schmidt
Affiliations:
Department of Computing Science, University of Alberta, Edmonton, AB, Canada;Department of Computing Science, University of Alberta, Edmonton, AB, Canada;Department of Computing Science, University of Alberta, Edmonton, AB, Canada;Department of Radiation Oncology, Cross Cancer Institute, Edmonton, AB, Canada;Department of Computing Science, University of Alberta, Edmonton, AB, Canada
Venue:
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Year:
2006

Citing 3
Cited 0

Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
Information Retrieval

Information Retrieval
Support vector random fields for spatial classification

PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.