Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, 50, UNIST-gil, Ulsan, 44919, Republic of Korea.
Steinfeld Co., 75 Clarendon Ave, San Francisco, 94114, CA, USA.
Comput Biol Med. 2024 Dec;183:109220. doi: 10.1016/j.compbiomed.2024.109220. Epub 2024 Oct 3.
The generation of depth images of occlusal dental crowns is complicated by the need for customization in each case. To decrease the workload of skilled dental technicians, various computer vision models have been used to generate realistic occlusal crown depth images with definite crown surface structures that can ultimately be reconstructed to three-dimensional crowns and directly used in patient treatment. However, it has remained difficult to generate images of the structure of dental crowns in a fluid position using computer vision models. In this paper, we propose a two-stage model for generating depth images of occlusal crowns in diverse positions. The model is divided into two parts: segmentation and inpainting to obtain both shape and surface structure accuracy. The segmentation network focuses on the position and size of the crowns, which allows the model to adapt to diverse targets. The inpainting network based on a GAN generates curved structures of the crown surfaces based on the target jaw image and a binary mask made by the segmentation network. The performance of the model is evaluated via quantitative metrics for the area detection and pixel-value metrics. Compared to the baseline model, the proposed method reduced the MSE score from 0.007001 to 0.002618 and increased DICE score from 0.9333 to 0.9648. It indicates that the model showed better performance in terms of the binary mask from the addition of the segmentation network and the internal structure through the use of inpainting networks. Also, the results demonstrated an improved ability of the proposed model to restore realistic details compared to other models.
生成牙冠的深度图像比较复杂，因为需要根据每个病例进行定制。为了减少熟练的牙科技术人员的工作量，已经使用了各种计算机视觉模型来生成具有明确牙冠表面结构的逼真的牙冠深度图像，这些图像最终可以重建为三维牙冠，并直接用于患者治疗。然而，使用计算机视觉模型生成处于运动状态下的牙冠结构的图像仍然具有一定的难度。在本文中，我们提出了一种用于生成不同位置牙冠深度图像的两阶段模型。该模型分为两个部分：分割和修复，以获得形状和表面结构的准确性。分割网络专注于牙冠的位置和大小，这使得模型能够适应不同的目标。基于 GAN 的修复网络根据目标颌骨图像和分割网络生成的二进制掩模生成牙冠表面的弯曲结构。通过区域检测和像素值指标对模型的性能进行了评估。与基线模型相比，所提出的方法将均方误差（MSE）得分从 0.007001 降低到 0.002618，将 DICE 得分从 0.9333 提高到 0.9648。这表明，通过添加分割网络，该模型在二进制掩模方面表现出更好的性能，并且通过使用修复网络，该模型在内部结构方面表现出更好的性能。此外，与其他模型相比，所提出的模型在恢复真实细节方面的能力也得到了提高。
