In implant prosthesis treatment, the design of surgical guide requires lots
of manual labors and is prone to subjective variations. 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.

本文提出了一种基于深度学习的两流植入体位置回归框架，使用 IRP 作为植入区域探测器和 MSPENet 作为多尺度嵌入回归网络，并在牙种间各种间隙中采用了多尺度图像块来建立编码器以适应植入体位置预测问题。实验结果表明，该框架在牙种植入领域中表现优异。