Discovering the Language of Surgery
Speaker: Rene Vidal, Johns Hopkins University
Time and Location: Thursday 10am - 11am, May 2, Wolf Hall 318.
Refreshments served 9:45am.
Abstract: Recent technological advances, such as Robotic Minimally
Invasive Surgery, have changed the way in which surgery can be performed, resulting in better precision, smaller incisions and reduced recovery time. However, the steep learning curve together with the lack of fair, objective, and effective criteria for judging the skills acquired by a trainee may reduce the benefits of this technology.
In this talk, I will present methods based on sparse dictionary learning, dynamical systems and computer vision to model surgeon expertise and perform automatic skill assessment and gesture classification from both kinematic and video data. The key idea is to decompose a surgical task into a series of pre-defined surgical gestures, such as "insert a needle", "grab a needle", "position a needle", etc., which should appear in some pattern, e.g., one gesture often follows another one, or several gestures form a motif. Different surgeons with different expertise will either execute different gestures differently or follow a slightly different sequence of gestures. This is analogous to what we see in natural language, where the grammar constrains the generation of words. The main difference is that, in the case of surgery, we know neither the words nor the grammar. Thus, there is a need to develop methods for discover the "language of surgery."
Bio:
Professor Vidal received his B.S. degree in Electrical Engineering (highest honors) from the Pontificia Universidad Catolica de Chile in 1997 and his M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences from the University of California at Berkeley in 2000 and 2003, respectively. He is currently an Associate Professor in the Center for Imaging Science in the Department of Biomedical Engineering of The Johns Hopkins University. His research interest are biomedical image analysis, computer vision, machine learning, hybrid systems, robotics and signal processing. Dr. Vidal has received numerous awards for his work, including the IAPR 2012 J.K. Aggarwal Prize for ``outstanding contributions to generalized principal component analysis (GPCA) and subspace clustering in computer vision and pattern recognition", the 2012 Best Paper Award in Medical Robotics and Computer Assisted Interventions, the 2012 Best Paper Award at the Conference on Decision and Control, the 2009 ONR Young Investigator Award, the 2009 Sloan Research Fellowship, the 2005 NFS CAREER Award and the 2004 Best Paper Award Honorable Mention at the European Conference on Computer Vision. He is Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence, the SIAM Journal on Imaging Sciences and the Journal of Mathematical Imaging and Vision, and has served as an area chair or program committee member for all major conferences in computer vision and medical imaging. He is a senior member of the IEEE and a member of the ACM.
Thursday, May 2, 2013 at 10:00am to 11:00am
Wolf Hall, Room 318
Wolf Hall, University of Delaware, Newark, DE 19716, USA
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