School of Mechanical Engineering, Shandong University, Jinan 250061, China.
School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China.
J Healthc Eng. 2022 Apr 12;2022:1933617. doi: 10.1155/2022/1933617. eCollection 2022.
Restoring the correct masticatory function of partially edentulous patient is a challenging task primarily due to the complex tooth morphology between individuals. Although some deep learning-based approaches have been proposed for dental restorations, most of them do not consider the influence of dental biological characteristics for the occlusal surface reconstruction. In this article, we propose a novel dual discriminator adversarial learning network to address these challenges. In particular, this network architecture integrates two models: a dilated convolutional-based generative model and a dual global-local discriminative model. While the generative model adopts dilated convolution layers to generate a feature representation that preserves clear tissue structure, the dual discriminative model makes use of two discriminators to jointly distinguish whether the input is real or fake. While the global discriminator focuses on the missing teeth and adjacent teeth to assess whether it is coherent as a whole, the local discriminator aims only at the defective teeth to ensure the local consistency of the generated dental crown.
Experiments on 1000 real-world patient dental samples demonstrate the effectiveness of our method. For quantitative comparison, the image quality metrics are used to measure the similarity of the generated occlusal surface, and the root mean square between the generated result and the target crown calculated by our method is 0.114 mm. In qualitative analysis, the proposed approach can generate more reasonable dental biological morphology.
The results demonstrate that our method significantly outperforms the state-of-the-art methods in occlusal surface reconstruction. Importantly, the designed occlusal surface has enough anatomical morphology of natural teeth and superior clinical application value.
由于个体之间牙齿形态复杂，使部分无牙患者的咀嚼功能恢复正常成为一项具有挑战性的任务。尽管已经提出了一些基于深度学习的方法用于牙科修复，但大多数方法都没有考虑到咬合面重建的牙齿生物学特征的影响。在本文中，我们提出了一种新的双鉴别器对抗学习网络来解决这些挑战。具体来说，该网络架构集成了两个模型：基于扩张卷积的生成模型和双全局-局部鉴别模型。生成模型采用扩张卷积层来生成保留清晰组织结构的特征表示，而双鉴别模型则利用两个鉴别器共同区分输入是真实的还是伪造的。全局鉴别器侧重于缺失牙齿和相邻牙齿，以评估其整体的连贯性，而局部鉴别器仅针对缺陷牙齿，以确保生成牙冠的局部一致性。
在 1000 个真实患者牙科样本上的实验证明了我们方法的有效性。为了进行定量比较，使用图像质量指标来衡量生成的咬合面之间的相似性，并且通过我们的方法计算的生成结果与目标牙冠之间的均方根误差为 0.114mm。在定性分析中，所提出的方法可以生成更合理的牙齿生物形态。
结果表明，我们的方法在咬合面重建方面明显优于最先进的方法。重要的是，所设计的咬合面具有足够的天然牙齿解剖形态和优越的临床应用价值。
