针对腹腔镜手术，提出个性化可变形点云配准方法，提升术中解剖引导精度。
Towards Patient-Specific Deformable Registration in Laparoscopic Surgery
- 用Transformer结合重叠估计与匹配模块，预测密集对应关系
- 合成数据上达45%匹配分数和92%内点率，显著优于传统方法
- 适合需要高精度术中导航的腹腔镜手术场景
不安全的外科护理是重大健康问题，常源于医生经验、技能和情境意识不足。将患者特异性3D模型融入手术视野可增强可视化、提供实时解剖指导并减少术中并发症。然而，由于术前与术中器官表面存在形变和噪声等差异，通用手术中的可靠模型配准仍具挑战。为此，我们提出首个面向患者的非刚性点云配准方法，采用新型数据生成策略优化个体化结果。该方法结合Transformer编码器-解码器架构、重叠估计与专用匹配模块，预测密集对应关系，并通过物理驱动算法完成注册。在合成与真实数据上的实验表明，本方法显著优于传统无关个体的方法，在合成数据上实现45%匹配分数与92%内点率，展现出提升外科护理的潜力。
原文摘要 · Abstract (English)
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.
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