A Classification-based Glioma Diffusion Model Using MRI Data Marianne Morris 1,2 Russ Greiner 1,2 , Jörg Sander 2 , Albert Murtha 3 , Mark Schmidt 1,2 1 Alberta Ingenuity Centre for Machine Learning 2 University of Alberta 3 Cross Cancer Institute, Alberta Cancer Board
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A Classification-based Glioma Diffusion Model Using MRI Data Marianne Morris 1,2 Russ Greiner 1,2, Jörg Sander 2, Albert Murtha 3, Mark Schmidt 1,2 1 Alberta.
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A Classification-based Glioma Diffusion Model Using MRI Data
Marianne Morris1,2
Russ Greiner1,2, Jörg Sander2, Albert Murtha3, Mark Schmidt1,2
1 Alberta Ingenuity Centre for Machine Learning 2 University of Alberta3 Cross Cancer Institute, Alberta Cancer Board
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Predict Tumour Growth
Why? Study tumour growth patterns Improve treatment planning
initial tumour tumour 6 months later
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Outline
Introduction Incremental Growth Modeling
Features Models (UG, GW, CDM)
Experiments
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Incremental Growth Model
Iteratively assign each voxel around the active tumour border to tumour vsnon-tumour
Stops at termination condition Reaching a specified size of tumour … there’s no more voxels to add
Several Approaches
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Incremental Growth Model
Tumor
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Incremental Growth Model
Tumor
Neighbours
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Incremental Growth Model
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- +
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+ + -
Tumor
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Incremental Growth Model
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+ + -
Tumor
Neighbours
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Incremental Growth Model
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- + -
- + -
- + + - -
+ +
Tumor
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Incremental Growth Model
- +
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- + -
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+ +
Tumor
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Which New Voxels to Add
UG: Uniform Growth GW: Growth based on tissue types CDM: Classification-based diffusion
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Tumour growth modeling – uniform diffusion (UG)
Radial uniform growth(in all directions alike)
Original tumour
Final tumour volume
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Tumour growth modeling –
White vs. Grey matter (GW)
A 5:1 ratio for diffusion in white matter vs. grey matter (Sawnson et al., 2000)
White matter Grey matter
Original tumourFinal tumour volume
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Tumour growth modeling
Uniform growth: Yes!
GW model: If White matter: Yes! If Grey matter: 20%
CDM model: “Learn” tumour
growth pattern
Am I a tumour?
voxel
Active tumour border
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Classification-Based Diffusion Model (CDM) Preprocessing
Patient features Tumour properties Voxel features Features of neighbouring voxels
A total of 75 features
patient
tumour
voxel
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Features: Patient
Age Correlation between age and glioma grade
(more aggressive tumours occur in older patients; benign tumours in children)
patient
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Features: Tumour
Area-volume ratio Volume increase between 2
scans Percentage of edema
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Features: Voxel Min Distance from tumour border Tissue type derived from template Tissue type derived from patient’s image Image intensities (T1, T1-contrast, T2) Template intensity Edema region Coordinates & Tissue Map Distance-Area ratio
tumour
voxel
tumour
voxel
tumour
edema
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Features: Neighbourhood
For each of 6 neighbours*
Edema Image intensities Tissue type derived from template Tissue type derived from patient’s
image
A neighbourhood in 3D is the 6 voxels immediately adjacent to some voxel v (not including diagonal ones)
10 36
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2
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6 neighbours
y
x
z
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Classification-Based Diffusion Model (CDM) Preprocessing