James S. Duncan, Ph.D.

Model-Based Strategies for Biomedical Image Analysis

Monday, March 28, 4:00 PM

Packard Lab room 466

Reception prior to talk in Packard Lobby

Abstract: The development of methods to accurately and reproducibly recover useful quantitative information from medical images is often hampered by uncertainties in handling the data related to image acquisition parameters, the variability of normal human anatomy and physiology, the presence of disease or other abnormal conditions, and a variety of other factors. This talk will review image analysis strategies that make use of models based on geometrical, physical/biomechanical and physiological information to help constrain the range of possible solutions in the presence of such uncertainty. The discussion will be focused by looking primarily at several problem areas in the realms of  i.) analysis of both structure and function from neuroimage data, ii.) cardiac function analysis, and iii.) work in the area of cellular image analysis.  The examples will mostly concentrate on problems of image segmentation and motion/deformation tracking, but will include a discussion of approaches aimed at integrating information from multiple data sources.  The presentation will include a description of the problem areas and visual examples of the image datasets being used, an overview of the mathematical techniques involved and a presentation of results obtained when analyzing actual patient image data using these methods. Emphasis will be placed on how image-derived information and appropriate modeling can be used together to address the image analysis and processing problems noted above. 

Bio:  Dr. James S. Duncan is the Ebenezer K. Hunt Professor of Biomedical Engineering, Professor of Diagnostic Radiology and Electrical Engineering at Yale University.  He is a fellow of IEEE and a fellow of the American Institute for Medical and Biological Engineering. His research efforts have been in the areas of computer vision, image processing, and medical imaging, with an emphasis on biomedical image analysis. His laboratory has focused on several problem areas within the realms of neuroimaging-based structure/function analysis, cardiac function analysis, and radiotherapy-based cancer treatment, along with some newer work in cellular and molecular image analysis from microscopy images.  He has been president of the International Society for Medical Image Computing and Computer Assisted Intervention (MICCAI) since 2007, and he is co-Editor-in-Chief of the Medical Image Analysis journal. He is also an associate editor for IEEE Transactions on Medical Imaging, and is on the editorial board of the Journal of Mathematical Imaging and Vision. 

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