Computer-aided prognosis of neuroblastoma: classification of stromal development on whole-slide images

Olcay Sertel; Jun Kong; Hiroyuki Shimada; Umit Catalyurek; Joel H. Saltz; Metin Gurcan

doi:10.1117/12.770666

17 March 2008 Computer-aided prognosis of neuroblastoma: classification of stromal development on whole-slide images

Olcay Sertel, Jun Kong, Hiroyuki Shimada, Umit Catalyurek, Joel H. Saltz, Metin Gurcan

Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 69150P (2008) https://doi.org/10.1117/12.770666
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Neuroblastoma is a cancer of the nervous system and one of the most common tumors in children. In clinical practice, pathologists examine the haematoxylin and eosin (H&E) stained tissue slides under the microscope for the diagnosis. According to the International Neuroblastoma Classification System, neuroblastoma tumors are categorized into favorable and unfavorable histologies. The subsequent treatment planning is based on this classification. However, this qualitative evaluation is time consuming, prone to error and subject to inter- and intra-reader variations and sampling bias. To overcome these shortcomings, we are developing a computerized system for the quantitative analysis of neuroblastoma slides. In this study, we present a novel image analysis system to determine the degree of stromal development from digitized whole-slide neuroblastoma samples. The developed method uses a multi-resolution approach that works similar to how pathologists examine slides. Due to their very large resolutions, the whole-slide images are divided into non-overlapping image tiles and the proposed image analysis steps are applied to each image tile using a parallel computation infrastructure developed earlier by our group. The computerized system classifies image tiles as stroma-poor or stroma-rich subtypes using texture characteristics. The developed method has been independently tested on 20 whole-slide neuroblastoma slides and it has achieved 95% classification accuracy.

Citation Download Citation

Olcay Sertel, Jun Kong, Hiroyuki Shimada, Umit Catalyurek, Joel H. Saltz, and Metin Gurcan "Computer-aided prognosis of neuroblastoma: classification of stromal development on whole-slide images", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69150P (17 March 2008); https://doi.org/10.1117/12.770666

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