Abstract
The use of intraoral scanners in the field of dentistry is increasing. In orthodontics, the process of tooth segmentation and rearrangement provides the orthodontist with insights into the possibilities and limitations of treatment. Although, full-arch scan data, acquired using intraoral scanners, have high dimensional accuracy, they have some limitations. Intraoral scanners use a stereo-vision system, which has difficulties scanning narrow interdental spaces. These areas, with a lack of accurate scan data, are called areas of occlusion. Owing to such occlusions, intraoral scanners often fail to acquire data, making the tooth segmentation process challenging. To solve the above problem, this study proposes a method of reconstructing occluded areas using a generative adversarial network (GAN). First, areas of occlusion are eliminated, and the scanned data are sectioned along the horizontal plane. Next, images are trained using the GAN. Finally, the reconstructed two-dimensional (2D) images are stacked to a three-dimensional (3D) image and merged with the data where the occlusion areas have been removed. Using this method, we obtained an average improvement of 0.004 mm in the tooth segmentation, as verified by the experimental results.
Highlights
Intraoral scanners are widely used in the diagnosis and fabrication of orthodontic appliances
This study aims to solve the occlusion problem by preprocessing the scan data obtained by intraoral scanners through the use of a generative adversarial network (GAN)
We proposed an automated method to reconstruct the missing data in the interdental area of teeth, scanned using an intraoral scanner by applying a GAN
Summary
Intraoral scanners are widely used in the diagnosis and fabrication of orthodontic appliances. Intraoral scanners have a problem of occlusions due to the stereo-vision system applied. This lowers the accuracy of the tooth’s anatomic form, and a segmentation of each tooth is required, which requires a lot of time and effort by the operator. As the visual treatment objective, patient’s dental plaster model is used to simulate the orthodontic treatment by dissecting each tooth from the plaster model and rearranging them into the desired position [1].
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