As the commercial surgical guide design software usually does not support the export of implant position for pre-implantation data, existing methods have to scan the post-implantation data and map the implant to pre-implantation space to get the label of implant position for training. Such a process is time-consuming and heavily relies on the accuracy of registration algorithm. Moreover, not all hospitals have paired CBCT data, limitting the construction of multi-center dataset. Inspired by the way dentists determine the implant position based on the neighboring tooth texture, we found that even if the implant area is masked, it will not affect the determination of the implant position. Therefore, we propose to mask the implants in the post-implantation data so that any CBCT containing the implants can be used as training data. This paradigm enables us to discard the registration process and makes it possible to construct a large-scale multi-center implant dataset. On this basis, we proposes ImplantFairy, a comprehensive, publicly accessible dental implant dataset with voxel-level 3D annotations of 1622 CBCT data. Furthermore, according to the area variation characteristics of the tooth's spatial structure and the slope information of the implant, we designed a slope-aware implant position prediction network. Specifically, a neighboring distance perception (NDP) module is designed to adaptively extract tooth area variation features, and an implant slope prediction branch assists the network in learning more robust features through additional implant supervision information. Extensive experiments conducted on ImplantFairy and two public dataset demonstrate that the proposed RegFreeNet achieves the state-of-the-art performance.

翻译：由于商业手术导板设计软件通常不支持导出种植体位置以供植入前数据使用，现有方法不得不扫描植入后数据并将种植体映射到植入前空间，以获取用于训练的种植体位置标签。这一过程耗时且高度依赖配准算法的准确性。此外，并非所有医院都拥有配对的CBCT数据，这限制了多中心数据集的构建。受牙医根据邻牙纹理确定种植体位置的启发，我们发现即使遮蔽种植体区域，也不会影响种植体位置的确定。因此，我们提出在植入后数据中遮蔽种植体，使得任何包含种植体的CBCT均可作为训练数据。这一范式使我们能够摒弃配准过程，并使得构建大规模多中心种植体数据集成为可能。在此基础上，我们提出了ImplantFairy——一个全面、公开可访问的牙科种植体数据集，包含1622例CBCT数据的体素级三维标注。此外，根据牙齿空间结构的区域变化特征及种植体的倾斜度信息，我们设计了一种倾斜感知的种植体位置预测网络。具体而言，设计了一个邻域距离感知（NDP）模块以自适应提取牙齿区域变化特征，并通过一个种植体倾斜度预测分支，借助额外的种植体监督信息辅助网络学习更鲁棒的特征。在ImplantFairy和两个公共数据集上进行的大量实验表明，所提出的RegFreeNet实现了最先进的性能。