In laparoscopic liver surgery, augmented reality technology enhances intraoperative anatomical guidance by overlaying 3D liver models from preoperative CT/MRI onto laparoscopic 2D views. However, existing registration methods lack explicit modeling of reliable 2D-3D geometric correspondences supported by latent evidence, leading to limited interpretability and potentially unstable alignment in clinical scenarios. In this work, we introduce Land-Reg, a correspondence-driven deformable registration framework that explicitly learns latent-grounded 2D-3D landmark correspondences as an interpretable intermediate representation to bridge cross-modal alignment. For rigid registration, Land-Reg embraces a Cross-modal Latent Alignment module to map multi-modal features into a unified latent space. Further, an Uncertainty-enhanced Overlap Landmark Detector with similarity matching is proposed to robustly estimate explicit 2D-3D landmark correspondences. For non-rigid registration, we design a novel shape-constrained supervision strategy that anchors shape deformation to matched landmarks through reprojection consistency and incorporates local-isometric regularization to alleviate inherent 2D-3D depth ambiguity, while a rendered-mask alignment enforces global shape consistency. Experimental results on the P2ILF dataset demonstrate the superiority of our method on both rigid pose estimation and non-rigid deformation. Our code will be available at https://github.com/cuiruize/Land-Reg.

翻译：在腹腔镜肝脏手术中，增强现实技术通过将术前CT/MRI三维肝脏模型叠加至腹腔镜二维视图，提升了术中解剖引导能力。然而，现有配准方法缺乏对潜在证据支持的可靠二维-三维几何对应关系的显式建模，导致临床场景中可解释性有限且配准稳定性不足。本研究提出Land-Reg——一种基于对应关系的可变形配准框架，通过显式学习以潜在证据为基础的二维-三维标志点对应关系作为可解释的中间表示，以桥接跨模态配准。针对刚性配准，Land-Reg采用跨模态潜在对齐模块将多模态特征映射至统一潜在空间。进一步提出具有相似度匹配的不确定性增强重叠标志点检测器，以鲁棒估计显式的二维-三维标志点对应关系。对于非刚性配准，我们设计了新颖的形状约束监督策略：通过重投影一致性将形状形变锚定于匹配的标志点，并引入局部等距正则化以缓解固有的二维-三维深度歧义，同时采用渲染掩码对齐来增强全局形状一致性。在P2ILF数据集上的实验结果表明，本方法在刚性姿态估计与非刚性形变任务上均具有优越性。代码发布于https://github.com/cuiruize/Land-Reg。