Towards Patient-Specific Deformable Registration in Laparoscopic Surgery

arXiv cs.CV / 4/16/2026

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Key Points

The paper addresses unsafe surgical outcomes by targeting improved visualization and real-time anatomical guidance through patient-specific 3D modeling in laparoscopic surgery.
It highlights a core technical barrier for non-rigid registration: unreliable alignment caused by deformations and noise between preoperative and intraoperative organ surfaces.
The authors propose a new patient-specific non-rigid point cloud registration method that predicts dense correspondences using a Transformer-based encoder-decoder plus overlap estimation and a dedicated matching module.
They finalize registration with a physics-based algorithm and evaluate on synthetic and real data, reporting strong gains over agnostic baselines (notably 45% Matching Score and 92% Inlier Ratio on synthetic data).
The results suggest the approach could enable more reliable patient-adapted surgical navigation compared with general, non-individualized registration methods.

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.