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Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans [article]

Tai-Hsien Wu, Chunfeng Lian, Sanghee Lee, Matthew Pastewait, Christian Piers, Jie Liu, Fang Wang, Li Wang, Chiung-Ying Chiu, Wenchi Wang, Christina Jackson, Wei-Lun Chao (+2 others)
2022 arXiv   pre-print
Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment.  ...  Our TS-MDL first adopts an end-to-end iMeshSegNet method (i.e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan.  ...  ACKNOWLEDGMENT This work is supported, in part, by the Ohio State University College of Dentistry, NIHNIDCR DE022816, and NSF#1938533.  ... 
arXiv:2109.11941v3 fatcat:thhndfz4abbp5nb6oz5gifanua

Dense Representative Tooth Landmark/axis Detection Network on 3D Model [article]

Guangshun Wei, Zhiming Cui, Jie Zhu, Lei Yang, Yuanfeng Zhou, Pradeep Singh, Min Gu, Wenping Wang
2021 arXiv   pre-print
The proposed network takes as input a 3D tooth model and predicts various types of the tooth landmarks and axes.  ...  This design choice and a set of added components make the proposed network more suitable for extracting sparse landmarks from a given 3D tooth model.  ...  Specifically, as shown in Fig. 2 , given the paired CBCT image and dental model scanned from a patient in clinics, we first adopt ToothNet [15] and TSegNet [49] to faithfully segment tooth and tooth  ... 
arXiv:2111.04212v2 fatcat:rz7xcmcsdrg6vmwyvn627k7wfa