Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation

Authors:
J. Kong;O. Sertel;H. Shimada;K. L. Boyer;J. H. Saltz;M. N. Gurcan
Affiliations:
Department of Electrical and Computer Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210, USA and Department of Biomedical Informatics, The Ohio State University, 3190 Gr ...;Department of Electrical and Computer Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210, USA and Department of Biomedical Informatics, The Ohio State University, 3190 Gr ...;Department of Pathology and Laboratory Medicine, Childrens Hospital Los Angeles and University of Southern California, Keck School of Medicine, 4650 Sunset Boulevard, Los Angeles, CA 90027, USA;Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA;Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, Columbus, OH 43210, USA;Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, Columbus, OH 43210, USA
Venue:
Pattern Recognition
Year:
2009

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Abstract

Neuroblastoma (NB) is one of the most frequently occurring cancerous tumors in children. The current grading evaluations for patients with this disease require pathologists to identify certain morphological characteristics with microscopic examinations of tumor tissues. Thanks to the advent of modern digital scanners, it is now feasible to scan cross-section tissue specimens and acquire whole-slide digital images. As a result, computerized analysis of these images can generate key quantifiable parameters and assist pathologists with grading evaluations. In this study, image analysis techniques are applied to histological images of haematoxylin and eosin (H&E) stained slides for identifying image regions associated with different pathological components. Texture features derived from segmented components of tissues are extracted and processed by an automated classifier group trained with sample images with different grades of neuroblastic differentiation in a multi-resolution framework. The trained classification system is tested on 33 whole-slide tumor images. The resulting whole-slide classification accuracy produced by the computerized system is 87.88%. Therefore, the developed system is a promising tool to facilitate grading whole-slide images of NB biopsies with high throughput.