in silico brain tumor research center emory university, atlanta, ga classification of brain tumor...
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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:
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)