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ImplantFormer: vision transformer-based implant position regression using dental CBCT data

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Abstract

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through fivefold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods.

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Data availability

The dental implant dataset is not publicly available due to privacy protection of the research participants.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 82261138629; Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515010688 and 2021A1515220072; Shenzhen Municipal Science and Technology Innovation Council under Grant JCYJ20220531101412030 and JCYJ20220530155811025.

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  1. Xinquan Yang and Xuguang Li have contributed equally to this work.

Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Xinquan Yang, Xuechen Li & Linlin Shen

  2. AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen, China

    Xinquan Yang, Xuechen Li & Linlin Shen

  3. National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China

    Xinquan Yang, Xuechen Li & Linlin Shen

  4. Department of Stomatology, Shenzhen University General Hospital, Shenzhen, China

    Xuguang Li & Yongqiang Deng

  5. School of Dentistry, Shenzhen University, Shenzhen, China

    Peixi Wu

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  1. Xinquan Yang
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Correspondence to Linlin Shen.

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Yang, X., Li, X., Li, X. et al. ImplantFormer: vision transformer-based implant position regression using dental CBCT data. Neural Comput & Applic 36, 6643–6658 (2024). https://doi.org/10.1007/s00521-023-09411-1

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