Robust surface registration using salient anatomical features in image-guided liver surgery

Abstract

A successful surface based image-to-physical space registration in image-guided liver surgery (IGLS) is critical to provide reliable guidance information and pertinent surface displacement data for use in deformation correction algorithms. The current protocol used to perform the image-to-physical space registration involves an initial pose estimation provided by a point based registration of anatomical landmarks identifiable in both the preoperative tomograms and the intraoperative presentation. The surface based registration is then performed via a traditional iterative closest point algorithm between the preoperative liver surface, segmented from the tomographic image set, and an intra-operatively acquired point cloud of the liver surface provided by a laser range scanner. Using the aforementioned method, the registration accuracy in IGLS can be compromised by poor initial pose estimation as well as tissue deformation due to the liver mobilization and packing procedure performed prior to tumor resection. In order to increase the robustness of the current surface-based registration method used in IGLS, we propose the incorporation of salient anatomical features, identifiable in both the preoperative image sets and intra-operative liver surface data, to aid in the initial pose estimation and play a more significant role in the surface based registration via a novel weighting scheme. The proposed surface registration method will be compared with the traditional technique using both phantom and clinically acquired data. Additionally, robustness studies will be performed to demonstrate the ability of the proposed method to converge to reasonable solutions even under conditions of large deformation and poor initial alignment.