发表机构
AI in Medicine and Surgery Group, School of Computer Science, University of Leeds; Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong; Department of HPB and Transplant Surgery, St. James’s University Hospital（利兹大学计算机学院医学与外科人工智能小组; 香港大学李嘉诚医学院放射诊断学系; 圣詹姆斯大学医院肝脏胰胆和移植外科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对AR引导腹腔镜手术中3D-2D肝脏配准难题，提出Vis2Reg框架，利用掩码一致可见区域约束变形，引入可见性感知自监督，结合刚性初始化模块与隐式神经变形场，在真实数据集上验证了准确性与效率。
AI 中文摘要
准确的3D-2D肝脏配准将术前3D模型与部分依赖视图的术中表面观察对齐，对AR引导的腹腔镜手术至关重要，但因严重遮挡、可见性有限和缺乏3D地面真值监督而具有挑战性。现有无标记方法进行部分到完全的几何对齐，在极端部分可见性下进行鲁棒的自我监督仍很困难。我们提出Vis2Reg，一个可见性感知配准框架，使用掩码一致的可见区域明确约束变形。我们引入了一种可见性感知自我监督，通过可微点光栅化和掩码引导的反向投影，从术中掩码中导出可见域3D监督信号。该公式提高了在严重遮挡下的鲁棒性，同时保持完全自我监督学习。Vis2Reg结合了一个鲁棒的几何刚性初始化模块和一个隐式神经变形场以实现稳定对齐。在真实术中数据集上，Vis2Reg的Dice分数达到92.6%，倒角距离为1.43毫米，每帧推理时间为111毫秒，展示了准确性和实际效率。
英文摘要
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.
Comments11 pages
Journal ref29th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'2026)
