TY - GEN
T1 - Computer-aided prognosis of neuroblastoma
T2 - Medical Imaging 2008 - Computer-Aided Diagnosis
AU - Sertel, Olcay
AU - Kong, Jun
AU - Shimada, Hiroyuki
AU - Catalyurek, Umit
AU - Saltz, Joel H.
AU - Gurcan, Metin
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Histopathological image analysis
KW - Multi-resolution image
KW - Neuroblastoma prognosis
KW - Processing
KW - Texture analysis
KW - Whole-slide image processing
UR - https://www.scopus.com/pages/publications/44349097393
U2 - 10.1117/12.770666
DO - 10.1117/12.770666
M3 - Conference contribution
AN - SCOPUS:44349097393
SN - 9780819470997
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2008 - Computer-Aided Diagnosis
Y2 - 19 February 2008 through 21 February 2008
ER -