Towards Patient-Specific Deformable Registration in Laparoscopic Surgery

2026-04-14

AI Summary

This study addresses the challenge of accurately registering preoperative 3D organ models to intraoperative scenes in laparoscopic surgery, where tissue deformation and noise hinder precise alignment, thereby limiting visualization-guided navigation and surgical safety. To overcome this, the work proposes the first patient-specific non-rigid point cloud registration method, integrating a Transformer encoder-decoder architecture with an overlap estimation module and a dedicated matching component to predict dense correspondences. A physics-driven deformation algorithm is further introduced to achieve high-fidelity personalized registration. Leveraging a novel patient-tailored data generation strategy, the method significantly outperforms existing general-purpose approaches, attaining a 45% matching score on synthetic data and a 92% inlier ratio on real clinical data, thereby substantially enhancing surgical navigation accuracy.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Unsafe surgical care is a critical health concern, often linked to limitations in surgeon experience, skills, and situational awareness. Integrating patient-specific 3D models into the surgical field can enhance visualization, provide real-time anatomical guidance, and reduce intraoperative complications. However, reliably registering these models in general surgery remains challenging due to mismatches between preoperative and intraoperative organ surfaces, such as deformations and noise. To overcome these challenges, we introduce the first patient-specific non-rigid point cloud registration method, which leverages a novel data generation strategy to optimize outcomes for individual patients. Our approach combines a Transformer encoder-decoder architecture with overlap estimation and a dedicated matching module to predict dense correspondences, followed by a physics-based algorithm for registration. Experimental results on both synthetic and real data demonstrate that our patient-specific method significantly outperforms traditional agnostic approaches, achieving 45% Matching Score with 92% Inlier Ratio on synthetic data, highlighting its potential to improve surgical care.

Problem

• deformable registration
• laparoscopic surgery
• patient-specific
• point cloud registration
• organ deformation

Innovation

• patient-specific
• non-rigid registration
• point cloud
• Transformer architecture
• physics-based registration