title: Toward detection and localization of instruments in minimally invasive surgery.
abstract: Methods for detecting and localizing surgical instruments in laparoscopic images are an important element of advanced robotic and computer-assisted interventions. Robotic joint encoders and sensors integrated or mounted on the instrument can provide information about the tool's position, but this often has inaccuracy when transferred to the surgeon's point of view. Vision sensors are currently a promising approach for determining the position of instruments in the coordinate frame of the surgical camera. In this study, we propose a vision algorithm for localizing the instrument's pose in 3-D leaving only rotation in the axis of the tool's shaft as an ambiguity. We propose a probabilistic supervised classification method to detect pixels in laparoscopic images that belong to surgical tools. We then use the classifier output to initialize an energy minimization algorithm for estimating the pose of a prior 3-D model of the instrument within a level set framework. We show that the proposed method is robust against noise using simulated data and we perform quantitative validation of the algorithm compared to ground truth obtained using an optical tracker. Finally, we demonstrate the practical application of the technique on in vivo data from minimally invasive surgery with traditional laparoscopic and robotic instruments.
authors.affiliation: Centre for Medical Image Computing and the Department of Computer Science, University College London, London, UK. maximilian.allan.11@ucl.ac.uk
authors.forename: Max
authors.initials: M
authors.lastname: Allan
authors.forename: Sébastien
authors.initials: S
authors.lastname: Ourselin
authors.forename: Steve
authors.initials: S
authors.lastname: Thompson
authors.forename: David J
authors.initials: DJ
authors.lastname: Hawkes
authors.forename: John
authors.initials: J
authors.lastname: Kelly
authors.forename: Danail
authors.initials: D
authors.lastname: Stoyanov
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019060:Minimally Invasive Surgical Procedures / Q000295:instrumentation* / Q000379:methods*; D015203:Reproducibility of Results; D012371:Robotics / Q000295:instrumentation*; D013525:Surgical Instruments