Perelman School of Medicine at the University of Pennsylvania


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Participating Labs

Computational Breast Imaging Group (CBIG)

The Computational Breast Imaging Group (CBIG) is a newly formed research group in the Breast Imaging Division of the Radiology department at the University of Pennsylvania. Our goal is to act as a translational catalyst between the worlds of computation imaging science and clinical breast cancer research by integrating image analysis, pattern recognition and data mining in clinically relevant breast imaging applications. We are developing an emerging research program to investigate the role of imaging as a quantitative biomarker for improving personalized clinical decision making for breast cancer screening, prognosis, and treatment. CBIG collaborates with faculty within Radiology, the Abramson Cancer Center, the Institute for the Translational Medicine and Therapeutics (ITMAT), and the Center for Clinical Epidemiology and Biostatistics (CCEB). Affiliated faculty includes experts in informatics, medical physics, genetics, pathology, oncology, biostatistics, epidemiology and primary care. Our vision is to foster a vibrant collaborative research environment in which basic scientists, graduate students, postdocs, medical trainees and clinical investigators will have the opportunity to work closely together to accelerate translation biomedical imaging research.

Center for Neuroimaging in Psychiatry (CNIP)

The Center for Neuroimaging In Psychiatry (CNIP) at the University of Pennsylvania is at the forefront of brain imaging in psychiatric research. The CNIP team acquires, processes and analyzes data in numerous modalities including: high (3T) and ultra high (7T) field Blood Oxygen Level Dependent functional Magnetic Resonance Imaging (BOLD fMRI), Diffusion Tensor Imaging (DTI), perfusion, Magnetic Resonance Spectroscopy (MRS), and Positron Emission Tomography (PET). This neuroimaging data is complemented with additional behavioral, phsyiological, and genetic information. All neuroimaging data is validated and organized into a customized XNAT database that archives and facilitates data processing. The CNIP is led by expert investigators collaborating with leading psychiatric researchers across the world with the goal of developing and implementing multi-modal techniques to advance the field of neuroimaging.

Path BioResource

Path BioResource was created by the Department of Pathology and Laboratory Medicine to provide administrative support to the departmentally-based shared resource laboratories. Through partnering with the School of Medicine administration, as well as Centers and Institutes within the University community, Path BioResource seeks to provide all investigators access to high quality, cost-effective advanced technology services as well as the scientific expertise to use these technologies effectively in their research efforts.

Penn Image Computing and Science Lab (PICSL)

A central factor in the success and increasingly wide-spread application of imaging-based approaches in medicine has been the emergence of sophisticated mathematical and computational methods for extracting, analyzing and integrating clinically significant and scientifically important information from image data. The Penn Image Computing and Science Laboratory is at the forefront of research and education in all of the quantitative methods represented, including segmentation, registration, morphometry and shape statistics, with numerous interdisciplinary collaborations spanning a variety of organ systems and all of the major and emerging modalities in biological/biomaterials imaging and in vivo medical imaging.

Penn Statistical Imaging and Visualization Endeavor (PennSIVE)

The Penn Statistical Imaging and Visualization Endeavor (PennSIVE) consists of a group of statisticians studying etiology and clinical practice through medical imaging. Based in the Center for Clinical Epidemiology and Biostatistics at the Perelman School of Medicine, we work closely with collaborators at the University of Pennsylvania and nationally in medical specialties including neurology, neurosurgery, and radiology. Our primary goals include 1)Developing robust and generalizable statistical methods for the analysis of multimodal biomedical imaging data. 2)Integrating complex medical imaging data and other biomarkers to study health. 3)Building clinical tools for the assessment of disease diagnosis, progression, and prognosis through cross-sectional and longitudinal imaging studies.

Section for Biomedical Image Analysis (SBIA)

The Section for Biomedical Image Analysis (SBIA) is devoted to the development of computer-based image analysis methods, and their application to a wide variety of clinical research studies. Image analysis methodologies include image registration, segmentation, population-based statistical analysis, biophysical modeling of anatomical deformations, and high-dimensional pattern classification. Clinical research studies span a variety of clinical areas and organs, and are performed within a wide network of collaborations from within and outside Penn. They include brain diseases such as Alzheimer's and schizophrenia, evaluation of treatment effects in large clinical trials, diagnosis of cardiac diseases, and diagnosis prostate, breast and brain cancer. SBIA also performs small animal imaging research aiming to understand brain development in mouse models.

Wang Lab

The Wang lab focuses on Alzheimer’s disease and other neurodegenerative disorders, aging, and psychiatric disorders including autism and bipolar disorder. Ongoing projects in the lab can be divided into the following three main directions: 1)Genetics and genomics of Alzheimer’s disease and other neurodegenerative disorders 2)Informatics and algorithm development for genome-scale experiments 3)Biomarker development for aging and neurodegenerative disorders. The Wang lab developed tools for the analysis of several large-scale genome-wide association (GWA) studies, which led to findings of new risk genes for frontotemporal dementia,progressive supranuclear palsy (PSP), and late-onset Alzheimer’s disease. The lab actively develops novel algorithms and computer programs that analyze GWA, DNA-seq and RNA-seq studies. The lab is also involved in biomarker development for aging and for Alzheimer's disease.