Endoscopic vision-based tracking of multiple surgical instruments during robot-assisted surgery

Abstract

Robot-assisted minimally invasive surgery is effective for operations in limited space. Enhancing safety based on automatic tracking of surgical instrument position to prevent inadvertent harmful events such as tissue perforation or instrument collisions could be a meaningful augmentation to current robotic surgical systems. A vision-based instrument tracking scheme as a core algorithm to implement such functions was developed in this study. An automatic tracking scheme is proposed as a chain of computer vision techniques, including classification of metallic properties using k-means clustering and instrument movement tracking using similarity measures, Euclidean distance calculations, and a Kalman filter algorithm. The implemented system showed satisfactory performance in tests using actual robot-assisted surgery videos. Trajectory comparisons of automatically detected data and ground truth data obtained by manually locating the center of mass of each instrument were used to quantitatively validate the system. Instruments and collisions could be well tracked through the proposed methods. The developed collision warning system could provide valuable information to clinicians for safer procedures.