Preoperative liver registration for augmented monocular laparoscopy using backward–forward biomechanical simulation

Abstract

Augmented reality for monocular laparoscopy from a preoperative volume such as CT is achieved in two steps. The first step is to segment the organ in the preoperative volume and reconstruct its 3D model. The second step is to register the preoperative 3D model to an initial intraoperative laparoscopy image. To date, there does not exist an automatic initial registration method to solve the second step for the liver in the de facto operating room conditions of monocular laparoscopy. Existing methods attempt to solve for both deformation and pose simultaneously, leading to nonconvex problems with no optimal solution algorithms. We propose in contrast to break the problem down into two parts, solving for (i)deformation and (ii)pose. Part (i)simulates biomechanical deformations from the preoperative to the intraoperative state to predict the liver's unknown intraoperative shape by modeling gravity, the abdominopelvic cavity's pressure and boundary conditions. Part (ii)rigidly registers the simulated shape to the laparoscopy image using contour cues. Our formulation leads to a well-posed problem, contrary to existing methods. This is because it exploits strong environment priors to complement the weak laparoscopic visual cues. Quantitative results with in silico and phantom experiments and qualitative results with laparosurgery images for two patients show that our method outperforms the state-of-the-art in accuracy and registration time.