Dimitris Metaxas, PhD
Director
John Nosher, MD
Clinical Co-Director
Computational Imaging provides software and algorithm development to support quantitative analysis of imaged pathology and radiology for both clinical and pre-clinical imaging applications. These resources and tools will enable Rutgers Cancer Institute investigators to detect, quantify and track tumor response to therapy in clinical trial settings, with the overall goal of stratifying patient populations, guiding clinical decisions and improving therapy planning.
Histopathology Imaging and Computational Image Analysis
Digital pathology technology enables biomedical researchers to digitize de-identified patient specimens and experimental specimens for easy storage, duplication, sharing and analysis. With the newly acquired high-throughput Olympus VS120 whole slide scanner, the Imaging Shared Resource can help users capture their experimental results in high-quality images for publication and analysis. The four-color fluorescent imaging capability allows fluorescent immunohistochemistry specimens to be digitized in their entirety, avoiding common fluorescent study pitfalls such as sampling error and photo-bleaching. State-of-art multispectral imaging not only captures entire emission spectrum of specimens in small wavelength intervals but also supports further fluorescent multiplexing by providing a means to separate emission signals originating from each fluorophors. Computational Imaging also provides consultation and help to projects related to imaging and image analysis to users. Researchers with specific analytical needs can receive custom analysis and software development service by working with staff at the service.
Radiological Imaging and Detecting and Tracking Tumor Response to Treatment
Advanced imaging and computational tools can serve to complement the growing number of precision medicine initiatives and programs that are underway at leading institutions throughout the country by facilitating objective, systematic monitoring and optimization of treatment and therapy plans which are tailored to meet the needs of individual patients.
Our team has been developing and evaluating a suite of newly developed computational and imaging tools for detecting and characterizing tumor response to treatment and improving the accuracy of predicting patient outcomes. Towards that end, our team proposes algorithms and methodologies which focus on neuroendocrine liver metastases, hepatic cell carcinoma, and colorectal carcinoma. These studies are supported by productive imaging research projects and correlated clinical studies which are already underway.
For more information or to request services, please contact:
Wenjin Chen, PhD
wenjin.chen@rutgers.edu