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Implant prosthesis is the most appropriate treatment for dentition defect ordentition loss, which usually involves a surgical guide design process todecide the implant position. However, such design heavily relies on thesubjective experiences of dentists. In this paper, a transformer-based ImplantPosition Regression Network, ImplantFormer, is proposed to automaticallypredict the implant position based on the oral CBCT data. We creatively proposeto predict the implant position using the 2D axial view of the tooth crown areaand fit a centerline of the implant to obtain the actual implant position atthe tooth root. Convolutional stem and decoder are designed to coarsely extractimage features before the operation of patch embedding and integratemulti-level feature maps for robust prediction, respectively. As bothlong-range relationship and local features are involved, our approach canbetter represent global information and achieves better location performance.Extensive experiments on a dental implant dataset through five-foldcross-validation demonstrated that the proposed ImplantFormer achieves superiorperformance than existing methods.

ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data