Real-time vision-based surgical tool segmentation with robot kinematics prior

Robot-assisted minimally invasive surgery combines the skills and techniques of highly-trained surgeons with the robustness and precision of machines. Several advantages include precision beyond human dexterity alone, greater kinematic degrees of freedom at the surgical tool tip, and possibilities in remote surgical practices through teleoperation. Nevertheless, obtaining accurate force feedback during surgical operations remains a challenging hurdle. Though direct force sensing using tool tip mounted sensors is theoretically possible, it is not amenable to required sterilization procedures. Vision-based force estimation according to real-time analysis of tissue deformation serves as a promising alternative. In this application, along with numerous related research in robotassisted minimally invasive surgery, segmentation of surgical instruments in endoscopic images is a prerequisite. Thus, a surgical tool segmentation algorithm robust to partial occlusion is proposed using DFT shape matching of robot kinematics shape prior (u) fused with log likelihood mask (Q) in the Opponent color space to generate final mask (U). Implemented on the Raven II surgical robot system, a real-time performance robust to tool tip orientation and up to 6 fps without GPU acceleration is achieved.