Cardiac surgical interventions often involve reconstructing complex structures on an arrested and flaccid heart under cardiopulmonary bypass. The relatively recent introduction of 4D (3D volumetric + time) ultrasound in pre- and intra-operative settings has opened the way to the development of tools to extract patient-specific information to help cardiac surgeons perform pre-operative planning and to predict the outcome of complex surgical interventions. In this talk, I describe techniques developed in a collaborative project between APL, the JHU SOM, and JHU BME dept., aimed at combining machine vision and modeling/simulation, to help surgeons tailor mitral valve surgical interventions (valvuloplasty) to specific patient conditions. At the end of the presentation, I will also review other recent collaboration projects in medical image analysis between the JHU APL and the JHU SOM.
Philippe Burlina is with the Johns Hopkins University Applied Physics Laboratory and the Department of Computer Science. He holds an M.S. and a Ph.D. in Electrical Engineering from the University of Maryland at College Park and a Diplome d’Ingenieur in Computer Science from the Universite de Technologie de Compiegne. His research interests span several areas of machine vision, hyperspectral imaging, medical image analysis, and Bayesian filtering.