IEEE J Biomed Health Inform. 2022 Jan;26(1):151-160. doi: 10.1109/JBHI.2021.3119394. Epub 2022 Jan 17.
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.
恢复破损牙齿的正确咀嚼功能是牙冠修复体康复的基础。然而，由于咬合面的复杂和个性化形态，这是一项具有挑战性的任务。在本文中，我们通过设计一个新的两阶段生成对抗网络（GAN），从数据驱动的角度来解决这个问题，对牙冠表面进行重建。具体来说，在第一阶段，设计了一个条件生成对抗网络（CGAN）来学习缺损牙齿和目标牙冠之间的内在关系，从而解决咬合关系恢复的问题。在第二阶段，通过考虑一个咬合槽解析网络（GroNet）和一个咬合指纹约束，进一步设计了一个改进的 CGAN，以强制生成器丰富咬合面的功能特征。实验结果表明，该框架在使用真实患者数据库的功能咬合面重建方面明显优于最先进的深度学习方法。此外，通过我们的方法计算出的生成的咬合面和目标牙冠之间的标准偏差（SD）和均方根误差（RMS）都小于 0.161mm。重要的是，设计的牙冠具有足够的解剖形态和更高的临床适用性。
