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Funded Research Projects

  • Scalable Automated Brain Tumor Segmentation: Brain tumor segmentation in Magnetic Resonance Imaging is an important task for neurosurgeons, oncologists, and radiologists to assess disease burden and measure tumor response to treatment. In 2008, over 237,000 individuals worldwide are estimated to have been diagnosed with malignant brain and central nervous system tumors with over 174,000 deaths. Detection of brain tumors with the exact location and orientation is extremely important for effective diagnosis, treatment planning, and analysis of treatment effectiveness; however, manual delineation of the tumor takes considerable time and is prone to error and wide variability. The overall goal of this proposal is to develop a scalable and automated approach for the segmentation of brain tumors based on Hidden Markov Models (HMMs). The objectives of the project are: 1) Develop a tumor segmentation approach based on a novel utilization of HMMs for automated segmentation of multi-sequence brain MRI data for accurate and robust determination of tumor volume; 2) Design a MapReduce model for the HMM-based brain tumor segmentation approach to enable scalable development of the segmentation processes in a cluster environment; 3) Evaluate the HMM-based brain tumor segmentation framework in terms of accuracy, robustness, and performance in the context of multi-sequence MRI data.

  • Data Mining Software for Large-Scale Analyses of Infections Caused by Hepatitis Viruses: The goal of this project is to develop data mining software that extracts, transforms, and loads structured data relating to infection with hepatitis viruses from diverse sources into a warehouse appropriate for mainframe, client/server, and PC platforms. This data will include but may not be limited to demographic, clinical, epidemiological, laboratory and phylogenetic information. The software will store and manage the data in a data warehouse system with a web-based interface to provide data access to the scientific community and analysis of relationships in the stored data using end-user defined queries to discover disease patterns and trends.  It is expected that the software will generate associations between epidemiological and laboratory data leading to the discovery of new disease patterns, epidemiological trends and proteomic associations. Such discoveries are expected to lead to new strategies for public health interventions, surveillance, prophylaxis and the development of antivirals and vaccines. This software tool will be applicable not only to hepatitis viruses but other pathogens in the areas of epidemiology, laboratory research and public health.

  • Information Integration of Heterogeneous Data Sources: Development of an information integration solution to provide a common interaction environment to query data and knowledge from multiple heterogeneous sources. Based on the latest Semantic Web technologies, it utilizes ontologies to address the integrated querying of sources of knowledge and information relevant to investigations of genotype-phenotype associations and to the identification of genes responsible for human diseases and conditions. [NIH RePORTER]

  • Semantic Data Integration for Integrative Cancer Biology Research: Development of a mechanism to formulate a coherent ontological view of caBIG semantics in order to perform ontology-based queries using the SPARQL query language over distributed caBIG-compatible data services. [NIH RePORTER]

  • Automated Development of Electronic Data Capture for Clinical Trials: Development of a set of graphical tools and automated software applications to simplify, automate, standardize, and reduce the cost of creating and reporting clinical research instruments used in substance abuse trials. [NIH RePORTER]

  • Administration and Management of Portable Electronic Forms in Clinical Environments (eForms): Development of a next-generation system for the development, administration and exchange of portable electronic clinical forms using XML and highly mobile platforms such as laptops and tablet computers. The product that resulted from this project is currently marketed under the name Aspect.

  • Integrated and Distributed Clinical Trials: Development and implementation of an electronic clinical trials system, an integrated clinical trial development and distributed administration framework to reduce the cost and increase the efficiency of creating and administering clinical trials. This system has recently been launched in the market with the name Aspect Trials.

  • A Mobile Clinical Trials Data Collection System: Development of mechanisms for the efficient and timely electronic capture of cancer clinical trials’ data using mobile handheld devices at the point of participant contact. The Mobile Clinical Trials Data Collection System interfaces directly with a clinical trial data management system (CDMS), and supports the CDISC ODM standard.

Related Links

Centers for Disease Control and Prevention (CDC)

National Center for Research Resources (NCRR)

National Cancer Institute (NCI)

National Institute on Drug Abuse (NIDA)

National Institute of Mental Health (NIMH)

  • Diagnostic Interview of Genetic Studies (DIGS)

 

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