Accurate and robust localization of medical devices can provide valuable information to improve clinical workflow and to facilitate the operations in image guided interventions. In this talk, I will discuss existing methods for tracking devices in interventions. I will then talk about our activities in this area which uses machine-learning based approaches for device detection and tracking. Several high impact applications would be discussed with quantitative validation results.
Terrence Chen received his M.S. and Ph.D. degree in computer science from University of Illinois at Urbana Champaign in 2002 and 2006, respectively. He got his B.S. degree in Computer Science and Information Engineering department from National Taiwan University in 1998. Currently he is a research program manager at Siemens Corporate Research at Princeton, New Jersey. He leads a group of research scientists focusing on the research and development for video analysis and understanding for various applications, including computer assisted interventions, video surveillance, screening, crowd estimation, histopathology, etc. His research interests include object recognition, visual learning, motion and tracking, visual surveillance and biometrics, and medical imaging where he has published more than 25 peer reviewed scientific papers.