Presentation at MICCAI 2015 related to classifying low grade gliomas.

1. Joel Carlson, Sung-Joon Ye
MSc Student
Radiological Physics Lab
Seoul National University
A Radiomics Approach to the
Classification of Astrocytoma
and Oligodendroglioma
http://joelcarlson.me
@jnkcarlson

2. Contents
Motivation
Methods
Types of features calculated
Image processing
Training sample creation
Model training and validation
Exploration of Results
Conclusions

3. Is it reproducible?
Not really.
-Concordance index among 4 experienced
neuropathologists: 52-70%
The Challenge and Problem
Correct classification of glioma category essential for
physicians and patients:
-Therapy choices
-Understanding prognosis
Oligodendroglioma
Astrocytoma
*
*
Current Method:
Neuropathologists determine classification
-Based on biopsy specimen
-Relatively subjective

4. Contents
Motivation
Methods
Types of features calculated
Image processing
Training sample creation
Model training and validation
Exploration of Results
Conclusions

5. Types of Features
Wndchrm
-Raw image features
-Transformed image features
-Compound transform features
Texture via Radiomics R package
-First order features (energy, entropy, etc)
-GLCM, GLRLM (rotation invariant)
-Thibault matrices: GLSZM, MGLSZM
Nuclei features
-Area, perimeter, max. diameter, skewness, kurtosis, etc)
Raw Fourier Transform
LoG
Edge

6. Pathology: Regions of Interest
Raw image
Superior method: Tiling
Select 5 ROIs
For each ROI calculate
wndchrm features

7. Steps:
1. Raw image (each pathology ROI)
2. Color Deconvolution
a. Eosin
b. Hematoxylin
3. Thresholding
– Gaussian blur
– Fill holes
– Watershed
4. Nuclei detection
– Area greater than some constant
5. Apply mask to raw image
6. Calculate Nuclei features
Pathology: Nuclei Segmentation

8. Radiology: Extraction of tumor components
BraTumIa used to segment tumor
1. Mask raw image with classification
2. Extract masked region for each
component
3. Calculate Features
– Wndchrm
– Textures
For the slice that most heavily
expresses a given component:

9. Model Building
3 Phases in a loop:
1. Create random samples:
– Patients (training/validation)
– Data
2. Permute data and combine; train models for each
permutation
3. Validate models; make and save predictions

10. Phase 1: Sampling Patients and Data
Sample Training and validation patients:
Sample data types:
Radiological
Full Tumor
Edema
Necrotic
Enhancing
Non-Enhancing
T1c
FLAIR
Image Type:
Choose 1
Tissue Type:
Choose 1
Nuclei
Pathological
1
2
3
4
5
ROI:
Choose 1
Training • 75% of patients
Validation • 25% of patients

11. Phase 2: Creating Permutations and Training
*Generalized Linear Model (LOOCV to determine regularization)
Rwnd_Rtext
Rwnd_Pwnd
Rwnd_Nshape
Rtext_Pwnd
Rtext_Nshape
Nuclei
Pwnd_NShape
Radiological
Texture
Pathological
wndchrm
Radiological
wndchrm
Nuclei
Shape
Train Model
for each
permutation
*
Pre-Process
(Center, Scale,
PCA)
Make Test
Set Predictions
Make Validation
Set Predictions

12. Phase 3: Validation and Predictions
For each model trained:
• Calculate Cross validation accuracy on validation set (25% of patients)
• Save predictions of each model on testing set
Final Predictions
• Majority class as voted by
models with:
• CV accuracy > 85%
• More than 3 variables
included in model

13. Contents
Motivation
Methods
Types of features calculated
Image processing
Training sample creation
Model training and validation
Exploration of Results
Conclusions

14. Results – CV Accuracy Density Histograms
Mean CV Accuracy: 0.502
Only Non-Enhancing
shows predictive ability
greater than chance on
CV accuracy
Models with CV Acc > 0.85
used for predictions

15. Exploring the Non-Enhancing CV Accuracy
Mean CV Acc: 0.502
All Models
1.Mean CV Acc: 0.608
NonEnhancing

16. Exploring the Non-Enhancing CV Accuracy
Mean CV Acc: 0.502
All Models
1.Mean CV Acc: 0.608
NonEnhancing
2.Mean CV Acc: 0.651
NonEnhancing,
Select Models

17. Exploring the Non-Enhancing CV Accuracy
Mean CV Acc: 0.502
All Models
1.Mean CV Acc: 0.608
NonEnhancing
2.Mean CV Acc: 0.651
NonEnhancing,
Select Models
3.Mean CV Acc: 0.688
NonEnhancing,
Select Models,
T1c only

18. 4.Mean CV Acc: 0.734
NonEnhancing,
Rwnd_Rtext only,
T1c only
Exploring the Non-Enhancing CV Accuracy
Mean CV Acc: 0.502
All Models
1.Mean CV Acc: 0.608
NonEnhancing
2.Mean CV Acc: 0.651
NonEnhancing,
Select Models
3.Mean CV Acc: 0.688
NonEnhancing,
Select Models,
T1c only

19. Conclusions
Radiological WNDCHRM and Texture features provide
useful and orthogonal information
-Nuclei features show some promise (not discussed)
Majority of features not useful
Using PCA obfuscates which features may be useful
Better predictions may take into account dependence of CV accuracy
on number of variables
Mean CV accuracy of all models with >3 variables: ~0.5
Mean CV accuracy of select models* with > 16 variables: ~0.7
*Certain T1c Non-Enhancing models (Rtext, Rwnd, Rwnd_Rtext)

20. http://joelcarlson.me
@jnkcarlson
Thank you for your attention!

21. Four categories
• High contrast features
• Edges, connected components,
spatial distribution, size and
shape
• Polynomial decompositions
• A polynomial that
approximates the image to
some fidelity is generated.
Coefficients used as
descriptors.
• High contrast features
• Edges, connected components,
spatial distribution, size and
shape
• Pixel statistics
• Pixel intensities within the
image (histograms, moments)
The WNDCHRM Feature Set