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In Silico Brain Tumor Research Center Emory 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 Center Emory University, Atlanta, GA. In Silico Brain Tumor Research. molecular. neuroimaging. Integrated

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In Silico Brain Tumor Research Center Emory University, Atlanta, GA

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  1. Classification of Brain Tumor RegionsS. 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 Center Emory University, Atlanta, GA

  2. In Silico Brain Tumor Research molecular neuroimaging Integrated Analysis histology clincal\pathology Datasets: In Silico Research Centers of Excellence

  3. Morphometry of the Gliomas Nuclear Morphology: Oligodendroglioma Astrocytoma Vessel Morphology: Necrosis:

  4. Morphometric Analysis Scientific Queries PAIS Database Parallel Matlab ? (90+ Million Nuclei)

  5. Morphological Correlates of Genomic Analysis Nuclear Classification ? Nuclear Characterization Classical Region Filtering Proneural Nuclear priors Mesenchymal (Neoplastic Oligodendroglia, Neoplastic Astrocytes, Reactive Endothelial, ...) Class Summary Statistics Neural

  6. Morphological Correlates of Genomic Analysis ? Nuclear Characterization Nuclear Classification Tissue Classification Nuclear Priors Classical Proneural Mesenchymal Neural (Neoplastic Oligodendroglia, Neoplastic Astrocytes, Reactive Endothelial, ...) Class Summary Statistics

  7. 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.

  8. Tissue Classifier: Training For each class (texture classification): Training Regions Extract “Textures” Texton Library For each training region: Train Region Classifier Region “Textures” Texton Histogram SVM

  9. Tissue Classifier: Testing Test Region Texton Library Region Classification Region “Textures” Texton Histogram SVM

  10. Dataset • Human Annotated regions • 18 whole-slide images • Normal, GBM (IV), Astrocytoma (II & III), Oligodendroglioma (II & III), Oligoastrocytoma (II & III)

  11. Experiment and Results • 30 x 2 cross-validation • Randomly pick 50% data for training and 50% for testing. • Classification accuracy: • Average(correct regions / total regions)

  12. Extension: Region Masking

  13. Questions

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