The use of augmented reality in minimally invasive surgery has been the subject of much research for more than a decade. The endoscopic view of the surgical scene is typically augmented with a 3D model extracted from a preoperative acquisition. However, the organs of interest often present major changes in shape and location because of the pneumoperitoneum and patient displacement. There have been numerous attempts to compensate for this distortion between the pre- and intraoperative states. Some have attempted to recover the visible surface of the organ through image analysis and register it to the preoperative data, but this has proven insufficiently robust and may be problematic with large organs. A second approach is to introduce an intraoperative 3D imaging system as a transition. Hybrid operating rooms are becoming more and more popular, so this seems to be a viable solution, but current techniques require yet another external and constraining piece of apparatus such as an optical tracking system to determine the relationship between the intraoperative images and the endoscopic view. In this article, we propose a new approach to automatically register the reconstruction from an intraoperative CT acquisition with the static endoscopic view, by locating the endoscope tip in the volume data. We first describe our method to localize the endoscope orientation in the intraoperative image using standard image processing algorithms. Secondly, we highlight that the axis of the endoscope needs a specific calibration process to ensure proper registration accuracy. In the last section, we present quantitative and qualitative results proving the feasibility and the clinical potential of our approach.
Abstract. Robotic assistance in minimally invasive surgical interventions has gained substantial popularity over the past decade. Surgeons perform such operations by remotely manipulating laparoscopic tools whose motion is executed by the surgical robot. One of the main tools deployed is an endoscopic binocular camera that provides stereoscopic vision of the operated scene. Such surgeries have notably garnered wide interest in renal surgeries such as partial nephrectomy, which is the focus of our work. This operation consists of the localization and removal of tumorous tissue in the kidney. During this procedure, the surgeon would greatly benefit from an augmented reality view that would display additional information from the different imaging modalities available, such as pre-operational CT and intra-operational ultrasound. In order to fuse and visualize these complementary data inputs in a pertinent way, they need to be accurately registered to a 3D reconstruction of the imaged surgical scene topology captured by the binocular camera. In this paper we propose a simple yet powerful approach for dense matching between the two stereoscopic camera views and for reconstruction of the 3D scene. Our method adaptively and accurately finds the optimal correspondence between each pair of images according to three strict confidence criteria that efficiently discard the majority of outliers. Using experiments on clinical in-vivo stereo data, including comparisons to two state-of-the-art 3D reconstruction techniques in minimally invasive surgery, our results illustrate superior robustness and better suitability of our approach to realistic surgical applications.
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