  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 five-fold
cross-validation demonstrated that the proposed ImplantFormer achieves superior
performance than existing methods.
