TY - GEN
T1 - A comprehensive framework for classification of nuclei in digital microscopy imaging
T2 - 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
AU - Kong, Jun
AU - Cooper, Lee
AU - Wang, Fusheng
AU - Chisolm, Candace
AU - Moreno, Carlos
AU - Kurc, Tahsin
AU - Widener, Patrick
AU - Brat, Daniel
AU - Saltz, Joel
PY - 2011
Y1 - 2011
N2 - In this paper, we present a comprehensive framework to support classification of nuclei in digital microscopy images of diffuse gliomas. This system integrates multiple modules designed for convenient human annotations, standard-based data management, efficient data query and analysis. In our study, 2770 nuclei of six types are annotated by neuropathologists from 29 whole-slide images of glioma biopsies. After machine-based nuclei segmentation for whole-slide images, a set of features describing nuclear shape, texture and cytoplasmic staining is calculated to describe each nucleus. These features along with nuclear boundaries are represented by a standardized data model and saved in the spatial relational database in our framework. Features derived from nuclei classified by neuropathologists are retrieved from the database through efficient spatial queries and used to train distinct classifiers. The best average classification accuracy is 87.43% for 100 independent five-fold cross validations. This suggests that the derived nuclear and cytoplasmic features can achieve promising classification results for six nuclear classes commonly presented in gliomas. Our framework is generic, and can be easily adapted for other related applications.
AB - In this paper, we present a comprehensive framework to support classification of nuclei in digital microscopy images of diffuse gliomas. This system integrates multiple modules designed for convenient human annotations, standard-based data management, efficient data query and analysis. In our study, 2770 nuclei of six types are annotated by neuropathologists from 29 whole-slide images of glioma biopsies. After machine-based nuclei segmentation for whole-slide images, a set of features describing nuclear shape, texture and cytoplasmic staining is calculated to describe each nucleus. These features along with nuclear boundaries are represented by a standardized data model and saved in the spatial relational database in our framework. Features derived from nuclei classified by neuropathologists are retrieved from the database through efficient spatial queries and used to train distinct classifiers. The best average classification accuracy is 87.43% for 100 independent five-fold cross validations. This suggests that the derived nuclear and cytoplasmic features can achieve promising classification results for six nuclear classes commonly presented in gliomas. Our framework is generic, and can be easily adapted for other related applications.
KW - Nuclei classification
KW - diffuse glioma
KW - feature selection
KW - metadata model
KW - microscopy image analysis
UR - http://www.scopus.com/inward/record.url?scp=80055063990&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2011.5872833
DO - 10.1109/ISBI.2011.5872833
M3 - Conference contribution
AN - SCOPUS:80055063990
SN - 9781424441280
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 2128
EP - 2131
BT - 2011 8th IEEE International Symposium on Biomedical Imaging
Y2 - 30 March 2011 through 2 April 2011
ER -