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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.
Kong et al. (Tue,) studied this question.