Bidirectional Partial-to-Full Non-Rigid Point Set Registration with Non-Overlapping Filtering

In this paper, we introduce Bidirectional Non-Overlapping Filtering Network (Bi-NOFNet), which registers the partial intraoperative point set with full preoperative point set for computer-assisted interventions (CAI). Our contributions are three-folds. First, Bi-NOFNet adopts customised feature extractor to extract distinctive features from both point sets, with which the per-point overlap mask is predicted and the overlapping region is segmented for the preoperative point set. Furthermore, we propose two methods to filter out the non-overlapping regions, at feature-level (i.e., Bi-NOFNet(Feature)) and point-level (i.e., Bi-NOFNet (Point)). For these two methods, we develop supervised registration strategy where the ground-truth overlap mask and displacement vectors are employed, and weakly-supervised registration strategies where only the ground-truth overlap mask is available. Additionally, to fully utilise the information in both space, we propose a bidirectional registration mechanism, which predicts the displacement vectors associated with the intraoperative point set (i.e., the forward way) and those warpping the preoperative point set (i.e., the backward way). Experiments have been conducted on the proposed DeformMedShapeNet dataset that contains 615 different liver shapes. Extensive results demonstrate that Bi-NOFNet performs well for partial-to-full registration tasks under various scenarios of noise, overlap ratios and deformation levels, outperforming existing non-rigid registration approaches.