Anant Madabuhshi

Dr. Anant Madabhushi is the Director of the Center for Computational Imaging and Personalized Diagnostics (CCIPD), Department of Biomedical Engineering, Case Western Reserve University. Dr.
Madabhushi received his Masters in Biomedical Engineering from the University of Texas, Austin in 2000 and in 2004 he obtained his PhD in Bioengineering from the University of Pennsylvania. He joined the Department of Biomedical Engineering, Case Western Reserve University in 2012, having been on the Biomedical Engineering faculty at Rutgers University from 2005-2012. He is also a member of the Cancer Institute of New Jersey and an Adjunct Assistant Professor of Radiology at the Robert Wood Johnson Medical Center, NJ. He has been a Senior IEEE member since 2009, was made a Wallace H. Coulter Fellow in 2010, and is an Associate Editor of IEEE Transactions on Biomedical Engineering, BMC Cancer, and a Guest Editor for Medical Physics. He has been lead organizer of a number of workshops in histological image analysis (HIMA), most recently in MICCAI 2008, MICCAI 2009, MICCAI 2010, MICCAI 2011, and MICCAI 2012. Dr. Madabhushi has authored over 195 peer-reviewed publications in leading International journals and conferences in the areas of computer vision and medical image analysis. He has two patents and 15 pending patents in the areas of computer-aided diagnosis of prostate and breast cancer, and in digital pathology. He has been the recipient of a number of early career awards and is currently engaged as a PI and Co-PI in two Academic Industrial Partnership R01 grants funded by the NCI (R01CA136535, R01CA140772), both focused on cancer imaging. He has been continuously funded by the NIH and DOD since 2006. Dr. Madabhushi’s team has developed pioneering computer aided diagnosis, pattern recognition, image analysis tools for diagnosis and prognosis of different types of cancers (prostate, breast, medulloblastoma, oropharyngeal) based on quantitative and computerized histomorphometric image analysis of digitized histologic biopsy tissue specimens. This novel approach involves quantitatively mining the histologic image data for hundreds of image features via sophisticated image segmentation, feature extraction, machine learning and pattern recognition methods and then predicting the risk of disease recurrence and patient prognosis. In the context of this project Dr. Madabhushi will serve as the contact PI and will lend his expertise to integrating histopathology image analysis applications (including segmentation, feature extraction, and coregistering in vivo imaging with ex vivo histology) into the pathology image informatics platform (PIIP).