In Silico Brain Tumor Research CenterEmory University, Atlanta, GA

Classification of Brain Tumor Regions

S. Cholleti, *L. Cooper, J. Kong, C. Chisolm, D. Brat, D. Gutman, T. Pan, A. Sharma, C. Moreno, T. Kurc, J. Saltz

In Silico Brain Tumor Research

Datasets:

histology neuroimaging

clincal\pathology

IntegratedAnalysis

molecular

In Silico Research Centers of Excellence

Morphometry of the Gliomas

Oligodendroglioma Astrocytoma

NuclearMorphology:

VesselMorphology:

Necrosis:

Morphometric Analysis

PAIS DatabaseParallel Matlab

Scientific Queries

?(90+ Million Nuclei)

Morphological Correlates of Genomic Analysis

NuclearCharacterization

Region FilteringNuclear

Classification Nuclear priors

ClassSummary Statistics ?Proneural

Neural

Classical

Mesenchymal(Neoplastic Oligodendroglia,Neoplastic Astrocytes,Reactive Endothelial, ...)

Morphological Correlates of Genomic Analysis

NuclearCharacterization

Tissue Classification

NuclearClassification

Nuclear Priors

ClassSummary Statistics

?Proneural

Neural

Classical

Mesenchymal

(Neoplastic Oligodendroglia,Neoplastic Astrocytes,Reactive Endothelial, ...)

Region Classification

• Classify regions as normal or tumor– exclude nuclei in normal tissue regions

– conditional probabilities for nuclear classification

• texton approach– Multiple layers of classification add robustness

– Combines supervised and unsupervised classifiers

• References– Malik, J., Belongie, S., Shi, J., and Leung, T. 1999. Textons, contours and regions: Cue

integration in image segmentation. In Proceedings IEEE 7th International Conference on Computer Vision, Corfu, Greece, pp. 918–925.

– O. Tuzel, L. Yang, P. Meer, and D. J. Foran. Classification of hematologic malignancies using texton signatures. Pattern Anal. Appl., 10(4):277-290,2007.

– M. Varma and A. Zisserman. Texture classification: Are filter banks necessary? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 691-698, 2003.

Tissue Classifier: Training

Train Region Classifier

SVM

For each training region:

Extract “Textures

”

Training Regions

Texton Library

For each class (texture classification):

Region “Textures”

Texton Histogram

Tissue Classifier: Testing

Texton Library

SVM

Region “Textures”

Texton Histogram

Test Region

Region Classification

Dataset• Human Annotated regions

– 18 whole-slide images

– Normal, GBM (IV), Astrocytoma (II & III), Oligodendroglioma (II & III), Oligoastrocytoma (II & III)

Region type #

Normal 45

Astrocytoma 20

Oligodendroglioma 54

Oligoastrocytoma 29

Glioblastoma 18

Total 166

Experiment and Results

Experiment Classification accuracy (%)

Normal vs Tumor 98

Oligodendroglioma vs Oligoastrocytoma 86

Oligodendroglioma vs Astrocytoma 92

Olgiodendroglioma vs Glioblastoma 91

Oligoastrocytoma vs Astrocytoma 80

Oligoastrocytoma vs Gligoblastoma 76

Astrocytoma vs Glioblastoma 70

• 30 x 2 cross-validation• Randomly pick 50% data for training and 50%

for testing.• Classification accuracy:

Average(correct regions / total regions)

Extension: Region Masking

Questions