提出可见性感知的无地标3D-2D配准方法，提升腹腔镜手术中肝脏模型对齐精度。
Vis2Reg: Visibility-Aware Landmark-Free Geometric 3D--2D Registration for Liver Laparoscopy
- 利用可见区域约束变形，通过可微点栅格化实现自监督信号生成
- 在真实术中数据上达到92.6%的Dice分数和1.43mm的Chamfer距离
- 适合需要高精度实时配准的外科导航系统应用
准确的3D-2D肝脏配准对AR引导腹腔镜手术至关重要，但受严重遮挡、视野受限及缺乏3D真值监督影响，仍具挑战。现有无地标方法虽能完成部分到完整的几何对齐，但在极端局部可视条件下自监督仍难鲁棒。本文提出Vis2Reg，一种可见性感知的注册框架，通过掩码一致的可见区域显式约束形变。引入可见域自监督机制，利用可微点栅格化与掩码引导反投影，从术中掩码推导出3D监督信号，实现完全自监督学习。该方法结合鲁棒刚性初始化模块与隐式神经形变场，确保稳定对齐。在真实术中数据集上，每帧推理仅需111毫秒，取得92.6%的Dice分数与1.43mm的Chamfer距离，兼具高精度与实用性。
原文摘要 · Abstract (English)
Accurate 3D--2D liver registration, which aligns preoperative 3D models to partial, view-dependent intraoperative surface observations, is critical for AR-guided laparoscopic surgery but remains challenging due to severe occlusion, limited visibility, and the lack of 3D ground-truth supervision. Existing landmark-free approaches perform partial-to-complete geometric alignment, yet robust self-supervision under extreme partial visibility remains difficult. We propose Vis2Reg, a visibility-aware registration framework that explicitly constrains deformation using mask-consistent visible regions. We introduce a visibility-aware self-supervision that derives a visible-domain 3D supervision signal from intraoperative masks, enabled by differentiable point rasterization and mask-guided back-projection. This formulation improves robustness under severe occlusion while maintaining fully self-supervised learning. Vis2Reg combines a robust geometric rigid initialization module with an implicit neural deformation field for stable alignment. Vis2Reg achieves a Dice score of 92.6\% and a Chamfer Distance of 1.43 mm on real intraoperative datasets, with 111 ms per-frame inference time, demonstrating both accuracy and practical efficiency.
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