miccai 2015

Joel Carlson, Sung-Joon YeMSc StudentRadiological Physics LabSeoul National University

A Radiomics Approach to the Classification of Astrocytomaand Oligodendroglioma

http://joelcarlson.me @jnkcarlson

ContentsMotivationMethods

Types of features calculatedImage processingTraining sample creationModel training and validation

Exploration of ResultsConclusions

Is it reproducible?Not really.

-Concordance index among 4 experienced neuropathologists: 52-70%

The Challenge and ProblemCorrect 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

ContentsMotivationMethods

Types of features calculatedImage processingTraining sample creationModel training and validation

Exploration of ResultsConclusions

Types of FeaturesWndchrm

-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

Pathology: Regions of Interest

Raw image

Superior method: Tiling

Select 5 ROIs

For each ROI calculate wndchrm features

Steps:1. Raw image (each pathology ROI)2. Color Deconvolution

a. Eosinb. Hematoxylin

3. Thresholding– Gaussian blur– Fill holes– Watershed

4. Nuclei detection– Area greater than some constant

5. Apply mask to raw image6. Calculate Nuclei features

Pathology: Nuclei Segmentation2a 2b1

3

4

5

Radiology: Extraction of tumor components

BraTumIa used to segment tumor1. Mask raw image with classification2. Extract masked region for each

component

3. Calculate Features– Wndchrm– Textures

For the slice that most heavily expresses a given component:

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

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

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

RadiologicalTexture

Pathologicalwndchrm

Radiologicalwndchrm

NucleiShape

Train Model for each

permutation

*

Pre-Process(Center, Scale,

PCA)

Make Test Set Predictions

Make Validation Set Predictions

Phase 3: Validation and PredictionsFor 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

ContentsMotivationMethods

Types of features calculatedImage processingTraining sample creationModel training and validation

Exploration of ResultsConclusions

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

1

Exploring the Non-Enhancing CV AccuracyMean CV Acc: 0.502

All Models

1.Mean CV Acc: 0.608NonEnhancing

Exploring the Non-Enhancing CV Accuracy

2Mean CV Acc: 0.502

All Models

1.Mean CV Acc: 0.608NonEnhancing

2.Mean CV Acc: 0.651NonEnhancing, Select Models

Exploring the Non-Enhancing CV Accuracy

3Mean CV Acc: 0.502

All Models

1.Mean CV Acc: 0.608NonEnhancing

2.Mean CV Acc: 0.651NonEnhancing, Select Models

3.Mean CV Acc: 0.688NonEnhancing,Select Models,

T1c only

4.Mean CV Acc: 0.734NonEnhancing,

Rwnd_Rtext only,T1c only

Exploring the Non-Enhancing CV AccuracyMean CV Acc: 0.502

All Models

1.Mean CV Acc: 0.608NonEnhancing

2.Mean CV Acc: 0.651NonEnhancing, Select Models

3.Mean CV Acc: 0.688NonEnhancing,Select Models,

T1c only

4

ConclusionsRadiological 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)

http://joelcarlson.me @jnkcarlson

Thank you for your attention!

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