腹腔镜手术中点云配准的测试时自适应方法
Test Time Adaptation Methods for Point Cloud Registration in Laparoscopic Surgery
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- ETS Montreal（蒙特利尔理工学院）
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中文总结 AI 辅助
该研究针对腹腔镜手术中点云配准的域偏移问题，修改三类测试时自适应方法适配三维配准，经实验验证输入自适应是低延迟且误差降低效果稳定的最优方案。
中文摘要 AI 辅助
腹腔镜手术中的三维点云配准用于估计从术中视频重建的器官与术前网格之间的变换。由于真实数据无法获取真实变换，监督式网络在合成器官对上进行训练。测试阶段，真实重建结果与合成数据存在差异，且存在噪声、稀疏、遮挡等问题，这会降低对应关系估计的性能。测试时自适应（TTA）可减少这种域偏移，但现有方法主要依赖logits、熵、类原型或配准中不可用的缓存。配准还涉及成对输入，其非对称偏移主要影响术中云。我们分析并修改了三类最先进的TTA方法以适配三维配准：模型自适应、归一化自适应和输入自适应。我们分析了四种代表性方法，分别基于辅助任务模型更新、无反向传播的令牌清除、特征对齐和层归一化校准，对其进行修改以处理术前与术中点云间的非对称偏移，并替换基于分类的熵目标。使用在干净合成源数据上训练的基于对应关系的模型，我们在P2P和P2ILReg上评估对损坏的合成和真实目标数据的自适应。对于合成目标，我们应用8种损坏类型，包括均匀噪声和全局密度降低，共5个严重程度级别。所有方法均提升了P2P上的配准性能，而归一化自适应会降低P2ILReg上的性能。考虑到基于反向传播的自适应的计算开销，输入自适应是腹腔镜手术最有前景的选项，其推理延迟低，且在各数据集上均能持续降低误差。代码：this https URL
英文摘要
3D point cloud registration in laparoscopic surgery estimates the transformation between an intraoperative organ reconstructed from video and its preoperative mesh. Because ground-truth transformations are unavailable for real data, supervised networks are trained on synthetic organ pairs. At test time, real reconstructions differ from synthetic data and are noisy, sparse, and occluded, which degrades correspondence estimation. Test-time adaptation (TTA) can reduce this domain shift, but existing methods mainly rely on logits, entropy, class prototypes, or cache memories unavailable in registration. Registration also involves paired inputs with an asymmetric shift that primarily affects the intraoperative cloud. We analyse and modify state-of-the-art TTA methods from three families to 3D registration: model, normalization, and input adaptation. We analyze four representative approaches based on auxiliary-task model updates, backpropagation-free token purging, feature alignment, and layer-normalization calibration. We modify them to handle asymmetric shifts between preoperative and intraoperative point clouds and replace classification-based entropy objectives. Using a correspondence-based model trained on clean synthetic source data, we evaluate adaptation to corrupted synthetic and real target data on P2P and P2ILReg. For synthetic targets, we apply eight corruptions, including uniform noise and global density reduction, at five severity levels. All methods improve registration on P2P, whereas on P2ILReg only input adaptation reduces the error, while normalization adaptation degrades it. Considering the computational overhead of backpropagation-based adaptation, input adaptation is the most promising option for laparoscopic surgery, providing low inference latency and consistent error reductions across datasets. Code: https://github.com/ninaa-git/survey_pc_registration_tta
