In implant prosthesis treatment, the design of the surgical guide heavily relies on the manual location of the implant position, which is subjective and prone to doctor's experiences. When deep learning based methods has started to be applied to address this problem, the space between teeth are various and some of them might present similar texture characteristic with the actual implant region. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global-local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods.

翻译：在种植义齿治疗中，手术导板的设计高度依赖于人工定位种植体位置，这一过程具有主观性且易受医生经验影响。尽管深度学习方法已开始应用于解决该问题，但牙齿间隙存在差异，且部分牙齿区域可能与实际种植区呈现相似的纹理特征，这两个问题对种植体位置预测构成了重大挑战。本文提出了一种两流种植体位置回归框架（TSIPR），该框架由种植体区域检测器（IRD）与多尺度补丁嵌入回归网络（MSPENet）组成，以解决上述问题。为训练IRD，我们扩展了原始标注以提供额外的监督信息，这些信息包含更丰富的特征且不会增加标注成本。针对MSPENet，我们设计了多尺度补丁嵌入模块，使其能够自适应地从具有不同牙齿间隙的图像中提取特征。全局-局部特征交互模块用于构建MSPENet的编码器，该模块融合了Transformer与卷积操作以增强特征表示。在推理阶段，从IRD中提取的候选区域掩码用于优化MSPENet的预测结果。通过在牙科种植数据集上进行五折交叉验证的广泛实验表明，所提出的TSIPR框架性能优于现有方法。