Robust harmonic field based tooth segmentation in real-life noisy scanned mesh

Abstract

Dental segmentation plays an important role in prosthetic dentistry such as crowns, implants and even orthodontics. Since people have different dental structures, it is hard to make a general dental segmentation model. Recently, there are only a few studies which try to tackle this problem. In this paper, we propose simple and intuitive algorithms for harmonic field based dental segmentation method to provide robustness for clinical dental mesh data. Our model includes additional grounds to gum, a pair of different Dirichlet boundary conditions, and convex segmentation for post-processing. Our data is generated for clinical usage and therefore has many noise, holes, and crowns. Moreover, some meshes have abraded teeth which deter the performance of harmonic field due to its dramatic gradient change. To the best of our knowledge, the proposed method and experiments are the first that deals with real clinical data containing noise and fragmented areas. We evaluate the results qualitatively and quantitatively to demonstrate the performance of the model. The model separates teeth from gum and other teeth very accurately. We use intersection over union (IoU) to calculate the overlap ratio between tooth. Moreover, human evaluation is used for measuring and comparing the performance of our segmentation model to other models. We compare the segmentation results of a baseline model and our model. Ablation study shows that our model improves the segmentation performance. Our model outperforms the baseline model at the expanse of some overlap which can be ignored.