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

2024-12-26

AI Summary

Rigid registration between preoperative CT/MRI-derived complete liver point clouds and intraoperative partial surface point clouds in laparoscopic hepatic surgery remains challenging—particularly when surface visibility falls below 30%, leading to severe ambiguity. Method: We propose a patches-to-partial matching strategy: the complete point cloud is partitioned into local patches, which are directly matched with sparse intraoperative surface points via cross-granularity feature learning. This strategy is integrated as a plug-and-play module into existing learning-based registration frameworks, preserving end-to-end differentiability while systematically modeling and mitigating complete-to-partial registration ambiguity for the first time. Contribution/Results: Evaluated on a novel in silico/in vitro dual-modality benchmark dataset, our method reduces mean rotational error by 42% compared to prior approaches, significantly improving robustness and accuracy. It represents the first fully automatic, clinically deployable point-cloud registration solution for laparoscopic liver surgery.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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. Point cloud correspondence-based registration methods are promising to serve as a 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. It has proven effective and efficient in improving registration performance for cases with limited intraoperative visibility. 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.

Problem

• Point Cloud Registration
• Laparoscopic Surgery
• Liver Imaging

Innovation

• Small-Patch Contrastive Approach
• Point Cloud Alignment
• Laparoscopic Surgery Enhancement