@inproceedings{ Allan2015Image,

year={ 2015 },

booktitle={ Medical Image Computing and Computer Assisted Intervention },

title={ Image based surgical instrument pose estimation with multi-class labelling and optical flow },

keywords={ Vision; Robotics; Tool Tracking; },

author={ Allan, Maximilian; Chang, Ping-Lin; Ourselin, Sebastien; Hawkes, David J; Sridhar, Ashwin; Kelly, John; Stoyanov, Danail; },

abstract={ Image based detection, tracking and pose estimation of surgical instruments in minimally invasive surgery has a number of potential applications for computer assisted interventions. Recent developments in the field have resulted in advanced techniques for 2D instrument detection in laparoscopic images, however, full 3D pose estimation remains a challenging and unsolved problem. In this paper, we present a novel method for estimating the 3D pose of robotic instruments, including axial rotation, by fusing information from large homogeneous regions and local optical flow features. We demonstrate the accuracy and robustness of this approach on ex vivo data with calibrated ground truth given by surgical robot kinematics which we will also make available to the community. Qualitative validation on in vivo data from robotic assisted prostatectomy further demonstrates that the technique can function in clinical scenarios. }

}