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.

翻译：种植体修复是牙列缺损或牙列缺失最适宜的治疗方法，通常涉及手术导板设计过程以确定种植体位置。然而，这种设计高度依赖牙医的主观经验。本文提出了一种基于Transformer的种植体位置回归网络ImplantFormer，旨在基于口腔CBCT数据自动预测种植体位置。我们创新性地提出利用牙冠区域的二维轴向视图预测种植体位置，并通过拟合种植体中心线来获取牙根处的实际种植体位置。设计了卷积主干网络和解码器，分别用于在块嵌入操作前粗提取图像特征，以及整合多层级特征图以实现鲁棒预测。由于同时考虑了长程关系与局部特征，我们的方法能更好地表征全局信息，并实现更优的定位性能。在牙科种植体数据集上通过五折交叉验证的广泛实验表明，所提出的ImplantFormer相比现有方法取得了更优性能。