DCPR-GAN: Dental Crown Prosthesis Restoration Using Two-Stage Generative Adversarial Networks

Sukun Tian, Miaohui Wang, Ning Dai (Corresponding Author), Haifeng Ma (Corresponding Author), Linlin Li, Luca Fiorenza (Corresponding Author), Yuchun Sun, Yangmin Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Restoring the correct masticatory function of broken teeth is the basis of dental crown prosthesis rehabilitation. However, it is a challenging task primarily due to the complex and personalized morphology of the occlusal surface. In this article, we address this problem by designing a new two-stage generative adversarial network (GAN) to reconstruct a dental crown surface in the data-driven perspective. Specifically, in the first stage, a conditional GAN (CGAN) is designed to learn the inherent relationship between the defective tooth and the target crown, which can solve the problem of the occlusal relationship restoration. In the second stage, an improved CGAN is further devised by considering an occlusal groove parsing network (GroNet) and an occlusal fingerprint constraint to enforce the generator to enrich the functional characteristics of the occlusal surface. Experimental results demonstrate that the proposed framework significantly outperforms the state-of-the-art deep learning methods in functional occlusal surface reconstruction using a real-world patient database. Moreover, the standard deviation (SD) and root mean square (RMS) between the generated occlusal surface and the target crown calculated by our method are both less than 0.161 mm. Importantly, the designed dental crown have enough anatomical morphology and higher clinical applicability.
Original languageEnglish
Pages (from-to)151-160
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
Publication statusPublished - 1 Jan 2022


  • Dental crown restoration
  • generative adversarial networks
  • occlusal fingerprint
  • occlusal surface


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