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CIALAB Neuroblastoma Research to be presented in 2009 MICCAI Workshop

CIALAB Neuroblastoma research is going to be presented in 2009 MICCAI workshop on Optical Tissue Image Analysis in  Microscopy, Histopathology and Endoscopy (OPTIMHisE) which will be held in Imperial Collage, London, UK on September 24th (http://www.miccai2009.org/). The study titled "A Combined Computerized System for Classifying Digitized Whole-slide Neuroblastoma Histology: Model-based Structural Features," proposes a unified framework that combines the previously developed classification systems for the stromal development and the grade of differentiation and improves the whole-slide classification accuracy of by introducing a set of clinically driven structural features.  


Olcay Sertel, Umit V. Catalyurek, Hiroyuki Shimada, and Metin Gurcan, "A Combined Computerized System for Classifying Digitized Whole-slide Neuroblastoma Histology: Model-based Structural Features".


Abstract: Neuroblastoma (NB) is one of the most malignant tumors affecting infants. In clinical practice, histopathological examination of  tissue samples is required for further treatment planning. The International Neuroblastoma Pathology Committee adopted the Shimada system, which relies on several morphological characteristics of the tissue  such as the degree of Schwannian stromal development and the grade of  neuroblastic differentiation to categorize the tissue sample as either favorable or unfavorable histology. In this study, we presented a combined  computer-aided prognosis system that integrates these two diagnosis processes within one analysis framework. Exploiting the intensity and texture characteristics of H&E-stained tissues, we achieved the segmentation using the expectation maximization (EM) algorithm that adaptively  identifies the distributions of the eosinophilic and basophilic structures.  In addition to conventional texture features, we introduced a novel way  of constructing structural features that captures the high-level perceptual patterns. The developed system was tested with an independent set  of 34 whole-slide images and achieved a classification accuracy of 94.1%  (32/34).

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