BRAIN TUMOR INFORMATICS
IN SILICO SCIENCE
CLINICAL AND TRANSLATIONAL INFORMATICS
HIGH END COMPUTING
IMAGING INFORMATICS

HPC for Classification of Whole Slide Tissue Images

Share Last Updated: August 03, 2012


A new joint project between Emory’s Department of Biomedical Informatics, Oak Ridge National Laboratory, and Remote Data Analysis and Visualization Center (RDAV) at University of Tennessee proposes using image analysis pipelines, high performance computing mechanisms, and scalable I/O tools to carry out machine learning methods and correlative analyses on large volumes of image data. The objective is to enable systematic analyses of brain tumor histological features using very large whole slide tissue image datasets (involving tens of thousands of images and billions of micro-anatomic structures). This project is one of the latest in ongoing efforts to understand the mechanisms that underlie disease onset and progression in brain tumors. It is motivated by the vision of facilitating novel, data-driven approaches in healthcare for development of more effective prevention, diagnosis, and treatment strategies tailored to patients or patient groups.

The project utilizes the Nautilus platform at RDAV and the high performance computing methods and scalable I/O tools developed by the ORNL and Emory teams to carry out large scale, remote analyses of imaging data. This effort will allow scientists to execute very large-scale analyses, significantly extending the coverage of their studies of brain tumor morphology. The work will also benefit computer science systems research in areas of runtime support and optimizations for data staging, management and processing on large shared-memory and hybrid systems.

Involved personnel

Emory: Joel Saltz, MD, PhD; Tahsin Kurc, PhD; Michael Nalisnik; Lee Cooper, PhD; Jun Kong, PhD; Tony Pan; and George Teodoro, PhD.

Oak Ridge: Scott Klasky, PhD

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