  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.
