Resolving the Ambiguity of Complete-to-Partial Point Cloud Registration for Image-Guided Liver Surgery With Patches-to-Partial Matching.

In image-guided liver surgery, the initial rigid alignment between preoperative and intraoperative data, often represented as point clouds, is crucial for providing sub-surface information from preoperative CT/MRI images to the surgeon during the procedure. Currently, this alignment is typically performed using semi-automatic methods, which, while effective to some extent, are prone to errors that demand manual correction. Alternatively, correspondence-based point cloud registration methods further offer a promising fully automatic solution. However, they may struggle in scenarios with limited intraoperative surface visibility, a common challenge in liver surgery, particularly in laparoscopic procedures, which we refer to as complete-to-partial ambiguity. We first illustrate this ambiguity by evaluating the performance of state-of-the-art learning-based point cloud registration methods on our carefully constructed in silico and in vitro datasets. Then, we propose a patches-to-partial matching strategy as a plug-and-play module to resolve the ambiguity, which can be seamlessly integrated into learning-based registration methods without disrupting their end-to-end structure. This approach effectively improves registration performance, especially in low-visibility conditions, reducing registration errors to 6.7 mm ($-$29%) in silico and 12.5 mm ($-$40%) in vitro, compared to state-of-the-art performance achieved by Lepard of 9.5 mm and 20.7 mm, respectively. The constructed benchmark and the proposed module establish a solid foundation for advancing applications of point cloud correspondence-based registration methods in image-guided liver surgery. Our code and datasets will be released at https://github.com/zixinyang9109/P2P.