classification-based glioma diffusion modeling marianne morris
Post on 21-Dec-2015
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TRANSCRIPT
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Classification-based Glioma Diffusion Modeling
Marianne Morris
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Overview Introduction
Motivation Assumptions
Related Work Framework Contribution Results Conclusions
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Introduction
Task: Where to irradiate! What is a glioma? What is tumour diffusion
modeling? Brain Biology MRI Radiotherapy
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Task Goal: Effective radiotherapy of Brain Tumours
determine what region of brain to treat (irradiate) Problem:
Just targeting visible tumour cells is NOT enough… Must also kill “(radiologically) occult”
cancer cells surrounding tumour ! Current Approach:
Irradiate 2cm margin around tumour Not known if
this area contains occult cells ONLY this area contains occult cells
Treated area
?? Normal tissue+
Occult cells ??
tumour
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Better Approach
Locate brain tumours from MRI scan Predict “(radiologically) occult” cancer
cells surrounding tumour predictor learned from earlier MRI data sets
Treat tumour + predicted-occult region Meaningful as current techniques can zap
arbitrary shapes!
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Underlying assumptions Occult cells future tumour growth Probability of growth of tumour T into
adjacent voxel V is determined by properties of T: growth rate, histology properties of V: location, intensity, tissue type
Voxel properties are known throughout brain
Uniformity of brain tumour characteristics
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What is a glioma? A primary
brain tumour that originated from a cell of the nervous system
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Diffusion Model
Tumor
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Diffusion Model
Tumor
Neighbours
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Diffusion Model
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Tumor
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Diffusion Model
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Tumor
Neighbours
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Diffusion Model
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Tumor
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Diffusion Model
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Tumor
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Diffusion Model
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Tumor
Neighbours
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Brain Biology
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MRIMagnetic Resonance Imaging
Magnet
signal
Echo signaldetected
Signal reconstructedinto image
Signal intensity (on image) determined by T1, T2 relaxation times
Time line in minutes00: T2 scanning05: T1 scanning10: contrast15: T1-contrast scanning
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MRI – image views
Axial Sagittal Coronal
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MRI – image types
T1 T1-contrast T2
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Tissue differentiation on MRI scans
TissueT1-weighted
T2-weighted
Bone Dark Dark
Air Dark Dark
Fat Bright Dark
Water Dark Bright
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MRI – image types
T1 T1-contrast T2
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T1-Contrast scan (axial) Tumour is bright
white structure
Necrotic region is black structure dead cells in center
of tumour
Edema may surround tumour swelling of normal
tissue
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Radiotherapy
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Radiotherapy
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Current Treatment Region
Irradiate everything within 2 cm margin around tumour
… includes Occult cells Normal cells
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Better Treatment Region
Irradiate Tumour Occult cells Minimal number of
normal cells - minimize loss of brain function
Higher dose of radiation – smaller chance of recurrent cancer
Radiotherapy can zap arbitrary shapes!
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Overview
Introduction Related Work Framework Contribution Results Conclusions
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Related work
Modeling macroscopic glioma growth 3D cellular automata (Kansal et al., 2000)
Differential motility in grey vs. white matter (Swanson et al., 2002)
White matter tract invasion (Clatz et al., 2004)
Supervised treatment planning (Zizzari, 2004)
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Related work
3D cellular automata Describes the transition of cells within
the tumour from dividing to necrotic Does not assume uniform radial growth Does not account for biological factors Too simple to model real tumour
growthProliferating Inactive Necrotic
Kansal et al., 2000
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Related work
A 5:1 ratio in white vs. grey matter
Rate of change of tumour cell density =Diffusion of tumour cells + Growth of
tumour
Dw = 5 Dg
Swanson et al., 2000
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Related work White matter tract invasion – DTI*
Uses anatomical atlas of white fibers Initiates simulation from a tumour at time 1 Uses diffusion-reaction equation Evaluates results against tumour at time 2
Only one test patient (GBM)
*Diffusion Tensor Imaging
Clatz et al., 2004
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Related work
Modeling macroscopic GBM growth Differential equations; diffusion-reaction
Supervised treatment planning Predicts treatment volume using ANN Trains on control points in predicted
clinical volume vs. truth treatment volume Does not consider brain or patient info
Zizzari, 2004
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Overview
Introduction Related Work Framework Contribution Results Conclusions
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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling
Contribution
Preprocessing
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Framework
Noise Reduction
Spatial Registration
Intensity Standardization
Tissue Segmentation
Tumour Segmentation
Preprocessing
Feature Extraction
Classification
Tumour Diffusion Modeling
Contribution
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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling
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Noise reduction
Inter-slice intensity variation reduction Reduction of sudden changes in intensity
values across the slices of a scan Using Weighted Linear Regression
Intensity inhomogeneity reduction Reduction of a varying spatial field
across the scan – inherent to MR imaging Using Statistical Parametric Mapping
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Inter-slice intensity variation
Before inter-slice intensity variation reduction
After inter-slice intensity variation reduction
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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling
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Spatial registration Using Statistical Parametric Mapping*
Linear template registration Registering to same coordinate system
Non-linear warping Applying deformations to lineup to template
Spatial interpolation Filling inter-slice gaps and computing
intensities
*Algorithms specifically designed for the analysis and processing of MRI brain scans
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Spatial registration Template example
Colin Holmes template Average T2 template
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Spatial registration
Before registration
After registration
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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling
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Intensity Standardization
Reduction of intensity variations across scans
Using Weighted Linear Regression
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Intensity Standardization
After intensity standardization
Before intensity standardization
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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling
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Tissue segmentation
White matter Grey matter Cerebrospinal fluid
Using Statistical Parametric Mapping
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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling
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Tumour segmentation
Slice from patient’s scan Segmented tumour
Tumour contour drawn by human experts
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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling
Contribution
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Features
Patient features Tumour properties Voxel features Neighbourhood attributes
A total of 76 features
patient
tumour
voxel
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Features
Patient attributes Age
Correlation between age and glioma grade (more aggressive tumours occur in older patients; benign tumours in children)
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Features
Tumour properties Growth rate of tumour mass Percentage of edema Area-volume ratio Volume increase between 2 scans
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Features Voxel features
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
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Features
Neighbourhood* features 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
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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling
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Classification
Task description Training and testing data Classifiers
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Classification – Task description
Diffusion model that iteratively assigns each voxel around the active tumour border to tumour or non-tumour class Learn a classifier from data of 17
patients Test on unlabeled brain volume Use labels predicted by classifier as
input to diffusion algorithm
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Classification Training data
Sample of voxels in volume-difference between two scans including 2-voxel border around the volume at the 2nd time scan
Volume-pairs for 17 patients Total of ½ million voxels
We evaluate voxels encountered in diffusion process Cross-validation (17 patients)
Original tumour
Additional tumour growth
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Classification
Classifiers Naïve Bayes Logistic Regression Linear-kernel SVM
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Framework Noise reduction Spatial registration Intensity Standardization Tissue segmentation Tumour segmentation Feature extraction Classification Tumour growth modeling
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Tumour growth modeling
Uniform diffusion Growth based on tissue types Classification-based diffusion CDM
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Tumour growth modeling – uniform diffusion
Radial uniform growth(in all directions alike)
Original tumour
Final tumour volume
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Tumour growth modeling –
White vs. matter
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 –
CDM Based on…
Features of patient, tumour and voxels around the tumour
Labels predicted by classifier Number of tumour-voxel neighbours
pi = 1 – (1 – qi)k
pi is probability that voxel i becomes tumour
Learns qi by training
qi = PΘ(l (vi) = tumour | epatient,etumour,ei) k is # tumour-voxel neighbours
Uses probability threshold pi > 0.65
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Tumour growth modeling - CDM
Tumor
Neighbours
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Tumour growth modeling - CDM
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Tumor
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Tumour growth modeling - CDM
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Tumor
Neighbours
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Tumour growth modeling - CDM
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Tumor
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Tumour growth modeling - CDM
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Tumor
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Overview
Introduction Related Work Framework Contribution Results Conclusions
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Results
Evaluation measure Best case Average case Special cases Average P/R (CDM, UG, GW)
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Results (evaluation measure)
Precision|nt ∩ pt|
|pt|
Recall |nt ∩ pt|
|nt|nt = truth & pt = prediction ; Precision = Recall
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Results (Best case)
CDM beats UG by 20% and GW by 12%
True positivesFalse positivesFalse negatives
Didn’t predict other wing of butterfly
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Results (Average case)
CDM beats UG by 6% and GW by 8%
True positivesFalse positivesFalse negatives
Didn’t predict growth in lower brain
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Results (Special case)
CDM beats UG by 8% and GW by 2%
True positivesFalse positivesFalse negatives
Resection & Recurrence
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Results
Average Recall
45
50
55
60
65
CDM UG GW
Average Recall
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Results
Average Precision
45
50
55
60
65
CDM UG GW
Average Precision
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Results T-test: the probability
that the means are not significantly different
Paired data (same data sample; different models) P(CDM vs. UG) = 0.001 P(CDM vs. GW) = 0.001 P(UG vs. GW) = 0.034
X is the meanVar: the variancen: the number of samples
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Overview
Introduction Related Work Framework Contribution Results Conclusions
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Conclusions
Challenging problem Still feasible Future research directions
More expressive features Spectroscopy, DTI, genetic data
Larger dataset (treatment effect) Brain atlas (“highways” vs. “barriers”)
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Acknowledgements
Dr. Russ Greiner & Dr. Jörg Sander Dr. Albert Murtha (Radiation Oncology, CCI)
BTGP team Mark Schmidt Stephen Walsh Chi Hoon Lee Alden Flatt, Luiza Antonie, Gabi Moise
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References Clatz et al., 2004, In Silico Tumour Growth: Application
to Glioblastomas, MICCAI 2004, 337--345 Kansal et al., 2000, Simulated brain tumour growth
dynamics using a three-dimensional cellular automaton, J Theor Biol., 203:367--382
SPM (online) - http://www.fil.ion.bpmf.ac.uk/spm/ Swanson et al., 2000, A quantitative model for
differential motility of gliomas in grey and white matter, Cell Prolif., 33:317--329
Zizzari 2004, Methods on Tumor Recognition and Planning Target Prediction for the Radiotherapy of Cancer, PhD Thesis, University of Magdeburg
Schmidt 2005, Automatic Brain Tumour Segmentation, MSc thesis, University of Alberta