Feature-Guided Nonrigid 3-D Point Set Registration Framework for Image-Guided Liver Surgery: From Isotropic Positional Noise to Anisotropic Positional Noise

Abstract

Registration is an essential problem in image-guided surgery (IGS) since it brings different involved coordinate frames together. Nonrigid or deformable registration still faces many challenges, such as two point sets (PSs) are partially overlapped. To tackle the challenges in the nonrigid registration, we introduce a new two-step point-based registration pipeline that includes two steps. In the first step, the rigid transformation between the two spaces is recovered where the orientation vectors are adopted. In the second step, built on the nonrigid coherent point drift (CPD) approach, the anisotropic positional noise is also assumed. Registration results on the human liver verify the proposed approach' great improvements over the other methods. First, the rotation and translation are recovered with smaller error values than the existing methods. Second, our registration method's performance is much more robust to the partial overlapping between two PSs. Third, the two-step registration framework achieves the best performances in most test cases when there is a localization error in acquiring the intraoperative data. Note to Practitioners-A novel registration approach is presented for image-guided liver surgery (LGLS). Compared with existing nonrigid registration methods, two significant changes (or improvements) exist in the proposed registration framework: 1) the normal vectors are extracted and utilized in the rigid registration step and 2) the anisotropic positional uncertainties are considered. In both steps, the registration problems are formulated as a maximum likelihood (ML) problems and dealt with the expectation-maximization (EM) technique. In both steps, the matrix form of the updated positional covariance is provided and can speed up the computational process. The readers are reminded that with extra information and a more general positional error assumption, our approach demonstrates improved performances in the case of partial-to-full alignment.