RegFreeNet: A Registration-Free Network for CBCT-based 3D Dental Implant Planning

2026-01-21

AI Summary

This study addresses the limitations of conventional dental implant localization methods, which rely on time-consuming pre- and post-operative CBCT registration to obtain ground-truth labels—a process constrained by registration accuracy and poorly scalable to large multicenter datasets. To overcome this, the authors propose a registration-free training paradigm that enables direct use of any post-operative CBCT containing implants by masking the implant regions during training. They introduce ImplantFairy, the first large-scale public 3D dental implant dataset comprising 1,622 cases with voxel-level annotations, and develop a 3D convolutional network integrating a neighbor tooth distance perception (NDP) module and an implant inclination prediction branch. Experiments demonstrate state-of-the-art performance across ImplantFairy and two public benchmarks, significantly enhancing localization robustness and generalization capability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: 3D Computer Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• dental implant planning
• CBCT
• registration-free
• multi-center dataset
• implant position annotation

Innovation

• Registration-Free
• CBCT
• Dental Implant Planning
• Slope-Aware Network
• Multi-Center Dataset