Instrument State Recognition and Tracking for Effective Control of Robotized Laparoscopic Systems

Surgical robots are an important component for delivering advanced paradigm shifting technology such as image guided surgery and navigation. However, for robotic systems to be readily adopted into the operating room they must be easy and convenient to control and facilitate a smooth surgical workflow. This paper proposes a novel approach for naturally controlling the robot mounted laparoscope's position by detecting a surgical grasping tool and recognizing if its state is open or close. This approach does not require markers or fiducials and uses a machine learning framework for tool and state recognition which exploits naturally occurring visual cues. Furthermore a virtual user interface on the laparoscopic image is proposed that uses the surgical tool as a pointing device to overcome common problems in depth perception. Instrument detection and state recognition are evaluated on in-vivo and ex-vivo porcine datasets. To demonstrate the practical surgical application and real time performance the system is validated in a simulated surgical environment.