ETH

The Computer Vision Laboratory of the ETH Zurich works on the computer based interpretation of 2D and 3D image data. One part of the Lab investigates into recognition, tracking of objects and people, and active 3D techniques. The Medical Image Analysis and Visualization group concentrates on the development of image analysis, visualization and simulation methods, providing information technology tools for biomedical research and clinical patient care. A special focus is the support of the complete chain of medical interventions, starting from computer aided diagnosis through interventional planning, intra-operative image guided navigation and intelligent instrumentation to post-operative follow-up and surgical training and education. The group has collected significant experience in generating detailed, patient-specific anatomical and physiological models from radiological images and characterizing of the motion of organs under physiological conditions, like respiration. The Laboratory has participated in numerous projects of the EC IST/ICT Research Programmes since the 4th Framework, like most recently in the projects FUSIMO, Passport, Hamam, Intuition, Beaming, Immersence, Cobol, Dirac, Hermes, Scovis.


Key staff:

Gábor Székely authored over 150 peer reviewed journal and conference contributions. He is member of the Scientific Committees of several leading international conferences and the Editorial Boards of numerous journals on the field of computer aided surgery and radiology. He is currently full professor at ETH and director of the Swiss National Centre of Competence in Research on Computer Aided and Image Guided Medical Interventions.

Christine Tanner received her Master’s degree in electronics research in 1999 and PhD in medical image analysis in 2006 at the King’s College London. She joined the Computer Vision Laboratory in 2008. Her principle research interests lie in the field of medical image processing, in particular in non-rigid image registration and motion modelling. She is currently supervising four PhD students in the domain of tracking and prediction of respiratory motion for therapy planning and guidance.