Deformable Registration of a Preoperative 3D Liver Volume to a Laparoscopy Image Using Contour and Shading Cues

Abstract

The deformable registration of a preoperative organ volume to an intraoperative laparoscopy image is required to achieve augmented reality in laparoscopy. This is an extremely challenging objective for the liver. This is because the preoperative volume is textureless, and the liver is deformed and only partially visible in the laparoscopy image. We solve this problem by modeling the preoperative volume as a Neo-Hookean elastic model, which we evolve under shading and contour cues. The contour cues combine the organ's silhouette and a few curvilinear anatomical landmarks. The problem is difficult because the shading cue is highly nonconvex and the contour cues give curve-level (and not pointlevel) correspondences. We propose a convergent alternating projections algorithm, which achieves a 4% registration error.

A case study: impact of target surface mesh size and mesh quality on volume-to-surface registration performance in hepatic soft tissue navigation

“…Some of these methods rely on feature extraction, while others combine feature extraction with feature tracking (i.e., mosaicing). These approaches fit preoperatively extracted features directly into the intra-operative image data [1,7,27]. Although these approaches are different from the presented nonrigid volume-to-surface registration method we evaluate, we hypothesize that these approaches may benefit from sensitivity analyses that entail varying the signal-to-noise ratio, image resolution, and other limiting factors that may influence the registration performance.…”

“…Since our goal is to evaluate the effect of different factors on registration performance, we choose the physics-based shape matching or PBSM method as an example algorithm for volume-to-surface registration. Moreover, it outperforms other related work with reports of mean errors smaller than 1 mm [7,14]. Compared to other methods, it describes the non-rigid registration as an electrostatic-elastic problem and relies on the finite element method (FEM) [23].…”

“…The general idea is to solve the boundary value problem while taking into account displacement boundary conditions from the intra-operative surface. In this context, various methods rely on a biomechanical model [2,7,10,14]. Since our goal is to evaluate the effect of different factors on registration performance, we choose the physics-based shape matching or PBSM method as an example algorithm for volume-to-surface registration.…”

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

“…Unfortunately, none of them works in de facto conditions of a monocular laparoscopy procedure [2,3] but two recent semi-automatic methods [4,5]. However, the methods [4,5] still leave room for improvement. The method [4] takes neither gravity load nor the pneumoperitoneum pressure into account in registration.…”

“…However, the methods [4,5] still leave room for improvement. The method [4] takes neither gravity load nor the pneumoperitoneum pressure into account in registration. It computes registration using only the weak laparoscopy visual cues, which consequently degrades progressively in unseen parts of the liver.…”