title: Ultrasound-based tumor movement compensation during navigated laparoscopic liver interventions.
abstract: BACKGROUND
  Image-guided navigation aims to provide better orientation and accuracy in laparoscopic interventions. However, the ability of the navigation system to reflect anatomical changes and maintain high accuracy during the procedure is crucial. This is particularly challenging in soft organs such as the liver, where surgical manipulation causes significant tumor movements. We propose a fast approach to obtain an accurate estimation of the tumor position throughout the procedure.
  
  
  METHODS
  Initially, a three-dimensional (3D) ultrasound image is reconstructed and the tumor is segmented. During surgery, the position of the tumor is updated based on newly acquired tracked ultrasound images. The initial segmentation of the tumor is used to automatically detect the tumor and update its position in the navigation system. Two experiments were conducted. First, a controlled phantom motion using a robot was performed to validate the tracking accuracy. Second, a needle navigation scenario based on pseudotumors injected into ex vivo porcine liver was studied.
  
  
  RESULTS
  In the robot-based evaluation, the approach estimated the target location with an accuracy of 0.4 ± 0.3 mm. The mean navigation error in the needle experiment was 1.2 ± 0.6 mm, and the algorithm compensated for tumor shifts up to 38 mm in an average time of 1 s.
  
  
  CONCLUSIONS
  We demonstrated a navigation approach based on tracked laparoscopic ultrasound (LUS), and focused on the neighborhood of the tumor. Our experimental results indicate that this approach can be used to quickly and accurately compensate for tumor movements caused by surgical manipulation during laparoscopic interventions. The proposed approach has the advantage of being based on the routinely used LUS; however, it upgrades its functionality to estimate the tumor position in 3D. Hence, the approach is repeatable throughout surgery, and enables high navigation accuracy to be maintained.
authors.affiliation: Medical Robotics, Institute for Robotics and Cognitive Systems, University of Lübeck, Lübeck, Germany, shahin@rob.uni-luebeck.de.
authors.forename: Osama
authors.initials: O
authors.lastname: Shahin
authors.forename: Armin
authors.initials: A
authors.lastname: Beširević
authors.forename: Markus
authors.initials: M
authors.lastname: Kleemann
authors.forename: Alexander
authors.initials: A
authors.lastname: Schlaefer
mesh_terms: D000465:Algorithms; D000818:Animals; D021621:Imaging, Three-Dimensional; D010535:Laparoscopy / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging* / Q000601:surgery; D008114:Liver Neoplasms, Experimental / Q000000981:diagnostic imaging / Q000601:surgery*; D025321:Surgery, Computer-Assisted / Q000379:methods*; D013552:Swine; D014463:Ultrasonography