Abstract
Delineating and removing brackets on 3D dental models and then reconstructing the tooth surface can enable orthodontists to pre-make retainers for patients. It eliminates the waiting time and avoids the change of tooth position. However, it is time-consuming and labor-intensive to process 3D dental models manually. To automate the entire process, accurate bracket segmentation and tooth surface reconstruction algorithms are of high need. In this paper, we propose a graph-based network named BSegNet for bracket segmentation on 3D dental models. The dynamic dilated neighborhood construction and residual connection in the graph network promote the bracket segmentation performance. Then, we propose a simple yet effective projection-based method to reconstruct the tooth surface. We project the vertices of the hole boundary on the tooth surface onto a 2D plane and then triangulate the projected polygon. We evaluate the performance of BSegNet on the bracket segmentation dataset and the results show the superiority of our method. The framework integrating the segmentation and reconstruction achieves a low reconstruction error and can be used as an effective tool to assist orthodontists in orthodontic treatment.
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Acknowledgement
This research was supported by Sichuan Univ. Interdisciplinary Innovation Res. Fund (RD-03-202108), Natural Science Fund of Hubei Province (2022CFB823), HUST Independent Inno. Res. Fund (2021XXJS096), Alibaba Innovation Research (AIR) program (CRAQ7WHZ11220001-20978282), and grants from MoE Key Lab of Image Processing and Intelligent Control.
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Tan, Y. et al. (2023). Coupling Bracket Segmentation and Tooth Surface Reconstruction on 3D Dental Models. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_40
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