Personalized design technique for the dental occlusal surface based on conditional generative adversarial networks

Abstract

The tooth defect is a frequently occurring disease within the field of dental clinic. However, the traditional manual restoration for the defective tooth needs an especially long treatment time, and dental computer aided design and manufacture (CAD/CAM) systems fail to restore the personalized anatomical features of natural teeth. Aiming to address the shortcomings of existed methods, this article proposes an intelligent network model for designing tooth crown surface based on conditional generative adversarial networks. Then, the data set for training the network model is constructed via generating depth maps of 3D tooth models scanned by the intraoral. Through adversarial training, the network model is able to generate tooth occlusal surface under the constraint of the space occlusal relationship, the perceptual loss, and occlusal groove filter loss. Finally, we carry out the assessment experiments for the quality of the occlusal surface and the occlusal relationship with the opposing tooth. The experimental results demonstrate that our method can automatically reconstruct the personalized anatomical features on occlusal surface and shorten the treatment time while restoring the full functionality of the defective tooth. K E Y W O R D S conditional generative adversarial networks, occlusal relationship, occlusal surface reconstruction, perceptual loss Fulai Yuan, Ning Dai, and Sukun Tian contributed equally to this study.

A tooth surface design method combining semantic guidance, confidence, and structural coherence

“…The statistics of the GFLOPs in the testing stage are also reported 2. The compared approaches are typical methods, including extensions of the GAN [1,8,9,30,36], approaches combining edge-based structural cues [3] and semantic cues [19,24,27,37]. The deep recurrent attentive writer (DRAW) [36] predicts the data content part-by-part through an attention mechanism, and the LAPGAN [30] learns in a coarse-to-fine manner.…”

“…However, the inferred map is unrealistic due to the lack of a geometric prior. An example of a crown design generated by using a conditional generative adversarial network (CGAN) [1] is illustrated in Figure 1c. To handle this challenge, some experts have explored the use of lowlevel structural information, for example, edges [2,3], to assist the prediction process.…”

Toward an artificial intelligence‐assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response

“…The two networks practically play a minimax game to reach Nash equilibrium, 35 where the goal of the generator is to maximize the probability of fooling the discriminator while the discriminator is trained to minimize the cross‐entropy loss between real and fake images. GANs have extensively been used in the computer vision community for image synthesis, image segmentation, and cross‐modality translations 36–38 . For example, Deep Convolutional GANs (DCGANs) 39 are employed to generate synthetic 2D images of liver lesions to assist training other DL models for lesion classification 40 .…”

“…GANs have extensively been used in the computer vision community for image synthesis, image segmentation, and cross‐modality translations. 36 , 37 , 38 For example, Deep Convolutional GANs (DCGANs) 39 are employed to generate synthetic 2D images of liver lesions to assist training other DL models for lesion classification. 40 A patch‐based GAN learning algorithm is trained to provide a cross‐modality framework for translation between CT and MRI images of brain.…”

A Dual Discriminator Adversarial Learning Approach for Dental Occlusal Surface Reconstruction

“…For example, Hwang et al [ 24 ] applied a Pix2pix [ 25 ]-based model for dental crown design. On this basis, Yuan et al [ 26 ] reconstructed the occlusal surface of the missing teeth by introducing perceptive loss and gap distance constraint. Tian et al [ 10 ] proposed a computer-aided deep adversarial-driven dental inlay restoration framework to automatically reconstruct the occlusal surface for a defective tooth.…”