A Classification-based Glioma Diffusion Model Using MRI Data

1. A Classification-based Glioma Diffusion Model Using MRI Data tumour voxel patient Original tumour Final tumour volume Tumor Uniform Growth GW Model Time 1 Time 2 Results True positives,False positives,False negatives Marianne Morris, Russ Greiner, Jörg Sander, Albert Murtha, Mark Schmidt Incremental Growth • Which new voxels to add? • UG: Uniform Growth • Grow equally in all directions • GW: Growth based on tissue types • Diffuse faster in white matter than grey matter • CDM: Classification-based diffusion • Learn key features from patient data and build a classifier to iteratively label voxels bordering the volume labelled “tumour” Problem Predict Tumour Growth Initial Tumour 6 Months Later • Why? • Improve Treatment Planning • CDM Features • Patient features • Tumour properties • Voxel features • Features of • neighbouring voxels • 75 in total • CDM Classifier • Voxel v becomes tumour given • qv = PΘ (class (v) = tumour | ep,et,ev) • Features of the patient ep • the tumour et • the voxel and its neighbours ev • Decision Threshold • If k tumour-voxel neighbours, then v becomes tumour with probability • pv = 1 – (1 – qv)k • Decision • Declare v is tumour if • pv0.65 • Stopping Criteria • Iterate around active tumour border until • tumour grown from initial volume at 1st scan equals size of tumour volume at 2nd scan • Precision = Recall • predicted volumetruth volume • Experiments • Training data • Volume-pairs for 17 patients • Total of ½ million voxels • We evaluate voxels encountered in diffusion process • Cross-validation (17 patients) CDM Performance Model Comparison • Conclusion • Learned model CDM performs significantly better than other existing models! • Can be improved with additional data • DTI, Spectroscopy, genetic data, brain atlas • For More Project Information, see • www.cs.ualberta.ca/~btgp Additional tumour growth Original tumour