In vivo estimation of target registration errors during augmented reality laparoscopic surgery

Abstract

PurposeSuccessful use of augmented reality for laparoscopic surgery requires that the surgeon has a thorough understanding of the likely accuracy of any overlay. Whilst the accuracy of such systems can be estimated in the laboratory, it is difficult to extend such methods to the in vivo clinical setting. Herein we describe a novel method that enables the surgeon to estimate in vivo errors during use. We show that the method enables quantitative evaluation of in vivo data gathered with the SmartLiver image guidance system.MethodsThe SmartLiver system utilises an intuitive display to enable the surgeon to compare the positions of landmarks visible in both a projected model and in the live video stream. From this the surgeon can estimate the system accuracy when using the system to locate subsurface targets not visible in the live video. Visible landmarks may be either point or line features. We test the validity of the algorithm using an anatomically representative liver phantom, applying simulated perturbations to achieve clinically realistic overlay errors. We then apply the algorithm to in vivo data.ResultsThe phantom results show that using projected errors of surface features provides a reliable predictor of subsurface target registration error for a representative human liver shape. Applying the algorithm to in vivo data gathered with the SmartLiver image-guided surgery system shows that the system is capable of accuracies around 12 mm; however, achieving this reliably remains a significant challenge.ConclusionWe present an in vivo quantitative evaluation of the SmartLiver image-guided surgery system, together with a validation of the evaluation algorithm. This is the first quantitative in vivo analysis of an augmented reality system for laparoscopic surgery.

Comparison of manual and semi-automatic registration in augmented reality image-guided liver surgery: a clinical feasibility study

“…A novel image-guided surgery system (SmartLiver) was designed for use in LLR through a programme of basic research and clinical development commissioned by the Wellcome Trust in partnership with the Department of Health (UK) [21,26,27,29].…”

“…Due to the lack of standardised methods for assessing IGS navigation accuracy [34], our group previously proposed a landmark based method which was also employed here [21,26]. In brief, distances between corresponding anatomical landmarks on laparoscopic images and the 3D liver model are measured and compared ( Fig.…”

“…The data from phase one were used to drive improvements to SmartLiver which were implemented in phase two test accuracy results for statistically significant differences between groups, independent and paired t-testing was used as appropriate. For in-depth details on accuracy evaluation please see [21,26].…”

Electromagnetic tracking in image‐guided laparoscopic surgery: Comparison with optical tracking and feasibility study of a combined laparoscope and laparoscopic ultrasound system

“…Image guidance, and specifically augmented reality, has been proposed as a way to alleviate this problem and reduce the risk of complication . Most guidance systems use optical tracking . When using optical tracking, to ensure a free line‐of‐sight, markers must be placed on the distal end of the laparoscope, far from the optical center of its camera, thereby creating a lever‐arm effect, reducing system accuracy.…”

“…1 Most guidance systems use optical tracking. [2][3][4][5] When using optical tracking, to ensure a free line-of-sight, markers must be placed on the distal end of the laparoscope, far from the optical center of its camera, thereby creating a lever-arm effect, reducing system accuracy. Our previous work has shown that an EM sensor placed at the proximal end of the laparoscope should provide higher system accuracy.…”