Objective

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Dental defect is a common problem in oral restoration treatment. For the symptoms of severe tooth loss

the traditional clinical treatment method is porcelain tooth restoration. This method primarily relies on the experience and technology of physicians and experts. Given the long time

this method leads to restoration precision is not easy to control. The current restoration method is to use the dental restoration computer aided design/computer aided manufacturing(CAD/CAM) system for restoration treatment. For example

in the CAD design of full-crown restorations

most methods use the standard tooth model in the standard database as the initial model to replace the defective tooth. A series of algorithms are used to properly deform the crown mesh model to reconstruct the target full-crown restoration model. This method uses the standard tooth model

lacks personalization

and requires a large number of man-machines in the design process. With regard to the interactive operation

the operator's technical experience is required

and the design contains a large degree of subjectivity. A personalized design method for the missing tooth crown based on the generative adversarial networks (HRGAN) is proposed to solve the personalized design of complex tooth morphology.

Method

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Given the powerful processing ability of the HRGAN on the image

the three-dimensional model is reduced to a two-dimensional image

and the network can learn it. First

the depth distance of the tooth is calculated by using the multi-direction orthogonal projection method

and the high-resolution depth images of the occlusal surface

lingual side

and buccal side were constructed. The quality of the tooth image is optimized by the method of pixel value enhancement to subsequently use the generative confrontation network and better fit the morphological distribution of the crown. Image entropy describes the richness of the information carried by the image. Image entropy is used to evaluate the constructed depth image and construct a batch of learning data sets suitable for deep convolutional networks. Then

based on the multi-scale generative and discriminant models

the structure of the missing tooth restoration network model is constructed. The generative model generates the crown data by capturing the distribution of the crown data. The discriminant model is used to determine whether a sample is from expert design data rather than generated data. The two models are trained alternately and iteratively

playing with each other and constantly optimizing. The network merges a feature-matching loss on the basis of the discriminator to better fit the morphological features of the teeth and adds a perceptual loss to enhance network's ability to reconstruct the shape of the occlusal surface of the tooth. Finally

the point cloud data of the crown are calculated on the basis of the gray value and distance value mapping

and the rough registration is performed according to the information of the missing crown in the adjacent tooth. The iterative closest point algorithm is used to precisely register the generated crown point cloud with the original point cloud and achieve the reconstruction of the missing tooth model.

Result

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We have constructed 500 sets of tooth depth map data

of which 400 sets are used as training sets and 100 sets are used as verification sets. The quality of the network-generated image is evaluated by the similarity between the tooth image synthesized from the HRGAN and the image obtained from the expert-designed tooth under experiments with different loss conditions. By calculating the peak signal to noise ratio(PSNR) and structrual similarity(SSIM) values of the two groups of images

the similarity between the images generated by the network and the image designed by the expert is improved after adding the loss of feature matching and perception

and the reconstructed occlusal surface of the crown has rich features of tooth pits and sulcus. The deviation analysis of the reconstructed occlusal surface and its corresponding expert design crown is carried out. The results show that compared with the CAD design method and the generative model-based design method

the standard deviation of the crown shape generated by the method in this paper is 21.2% and 7% lower than the former two methods

and the root mean square(RMS) value is 43.8% and 9.8% lower

respectively

and the shape of the occlusal surface of the crown is the closest to the shape of the expert-designed crown.

Conclusion

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The experimental results show that the tooth design method based on the high-resolution generation network proposed in this paper can effectively complete the morphological design of the missing tooth

and the designed crown has enough natural tooth anatomical morphological characteristics.