Contour-based surgical instrument tracking supported by kinematic prediction

Surgical tool tracking is an important key functionality for many high-level tasks in both robot-assisted and conventional minimally invasive surgery. Though the fields of application are similar in both surgery techniques (i.e. visually servoed instruments, workflow analysis or augmented reality), the kind of available information about the position and orientation of the surgical tool differ. In conventional laparoscopic surgery additional information to the images provides by the endoscopic camera can only be obtained by an external tracking system. In contrast, robotic systems provide angular informations from encoder readings that allow for a sufficient pose estimation and initialization of an image-based tracking algorithm. Our approach utilizes both encoder readings and visual information, in order to stabilize tracking in image space. The image-based tracking is supervised by means of the kinematic information and reinitialized in case of conflicting results. As tracking modality we utilize the Contracting Curve Density (CCD) algorithm that looks for maximal separation of local color statistics along the contour of a model.