Abstract

Accurate medical Augmented Reality (AR) rendering requires two calibrations, a camera intrinsic matrix estimation and a hand-eye transformation. We present a unified, practical, marker-less, real-time system to estimate both these transformations during surgery. For camera calibration we perform calibrations at multiple distances from the endoscope, pre-operatively, to parametrize the camera intrinsic matrix as a function of distance from the endoscope. Then, we retrieve the camera parameters intra-operatively by estimating the distance of the surgical site from the endoscope in less than 1 s. Unlike in prior work, our method does not require the endoscope to be taken out of the patient; for the hand-eye calibration, as opposed to conventional methods that require the identification of a marker, we make use of a rendered tool-tip in 3D. As the surgeon moves the instrument and observes the offset between the actual and the rendered tool-tip, they can select points of high visual error and manually bring the instrument tip to match the virtual rendered tool tip. To evaluate the hand-eye calibration, 5 subjects carried out the hand-eye calibration procedure on a da Vinci robot. Average Target Registration Error of approximately 7mm was achieved with just three data points.

Highlights

  • Augmented reality (AR) and mixed reality (MR) are valuable technologies for medical applications

  • In the past, multiple self-calibration methods have been proposed for intra-operative use, where the intrinsic camera calibration parameters can be estimated by using feature correspondences in the surgical scene [3, 4]

  • The root mean square error was calculated for each camera parameter

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Summary

Introduction

Augmented reality (AR) and mixed reality (MR) are valuable technologies for medical applications. MR/AR improves the hand-eye coordination for the surgeon [1], but requires two calibration steps to take pre-operative medical data to the intra-operative camera/endoscope feed. In the past, multiple self-calibration methods have been proposed for intra-operative use, where the intrinsic camera calibration parameters can be estimated by using feature correspondences in the surgical scene [3, 4]. Such methods cannot account for the change in lens distortion. The inability of new procedures to integrate seamlessly in the current existing surgical workflows is a major roadblock in translation of AR/MR surgical guidance systems to the operation theatre [6]

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