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This study focuses on classifying brain tumor regions by integrating molecular neuroimaging and histology datasets. The research involves morphometric analysis of gliomas, including nuclear and vessel morphology, necrosis, and genomic analysis. It utilizes nuclear classification, tissue classification, and neural morphological correlates for accurate classification. The approach combines supervised and unsupervised classifiers, making use of nuclear priors and region filtering for robustness. The research references methods like texton approach and integrates multiple layers of classification to enhance accuracy and reliability.
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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
In Silico Brain Tumor Research molecular neuroimaging Integrated Analysis histology clincal\pathology Datasets: In Silico Research Centers of Excellence
Morphometry of the Gliomas Nuclear Morphology: Oligodendroglioma Astrocytoma Vessel Morphology: Necrosis:
Morphometric Analysis Scientific Queries PAIS Database Parallel Matlab ? (90+ Million Nuclei)
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
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
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 For each class (texture classification): Training Regions Extract “Textures” Texton Library For each training region: Train Region Classifier Region “Textures” Texton Histogram SVM
Tissue Classifier: Testing Test Region Texton Library Region Classification Region “Textures” Texton Histogram SVM
Dataset • Human Annotated regions • 18 whole-slide images • Normal, GBM (IV), Astrocytoma (II & III), Oligodendroglioma (II & III), Oligoastrocytoma (II & III)
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)
