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

doi:10.1109/TMI.2022.3180343

	Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans
	Tai-Hsien Wu 1; Chunfeng Lian 2; Sanghee Lee 1; Matthew Pastewait 3; Christian Piers 4; Jie Liu 1; Fan Wang 5; Li Wang 6; Chiung-Ying Chiu 7; Wenchi Wang 7; Christina Jackson 7; Wei-Lun Chao 8; Dinggang Shen9,10 ; Ching-Chang Ko 1
	2022-11-01
发表期刊	IEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN	1558-254X
卷号	41 期号:11 页码:1-1
发表状态	已发表
DOI	10.1109/TMI.2022.3180343
摘要	Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. Manually performing these two tasks is time-consuming, tedious, and, more importantly, highly dependent on orthodontists’ experiences due to the abnormality and large-scale variance of patients’ teeth. Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). In contrast, the number of studies on tooth landmark localization is still limited. This paper proposes a two-stage framework based on mesh deep learning (called TS-MDL) for joint tooth labeling and landmark identification on raw intraoral scans. 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. Guided by the segmentation outputs, our TS-MDL further selects each tooth’s region of interest (ROI) on the original mesh to construct a light-weight variant of the pioneering PointNet (i.e., PointNet-Reg) for regressing the corresponding landmark heatmaps. Our TS-MDL was evaluated on a real-clinical dataset, showing promising segmentation and localization performance. Specifically, iMeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at ${0.964}\pm {0.054}$ , significantly outperforming the original MeshSegNet. In the second stage, PointNet-Reg achieved a mean absolute error (MAE) of ${0}.{597}\pm {0}.{761} \, mm$ in distances between the prediction and ground truth for 66 landmarks, which is superior compared with other networks for landmark detection. All these results suggest the potential usage of our TS-MDL in orthodontics.
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收录类别	EI ; SCI ; SCIE
来源库	IEEE
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/206328
专题	生物医学工程学院生物医学工程学院_PI研究组_沈定刚组
作者单位	1.Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA 2.School of Mathematics and Statistics, Xi’an Jiaotong University, Shaanxi, Xi’an, China 3.United States Air Force, Kadena, Japan 4.Small and Piers Orthodontics, Morganton, NC, USA 5.Department of Biomedical Engineering, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Shaanxi, Xi’an, China 6.Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA 7.SOVE Inc, Columbus, OH, USA 8.Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA 9.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China 10.Department of Research and Development, Shanghai United Imaging Intelligence Company Ltd, Shanghai, China
推荐引用方式 GB/T 7714	Tai-Hsien Wu,Chunfeng Lian,Sanghee Lee,et al. Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2022,41(11):1-1.
APA	Tai-Hsien Wu.,Chunfeng Lian.,Sanghee Lee.,Matthew Pastewait.,Christian Piers.,...&Ching-Chang Ko.(2022).Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans.IEEE TRANSACTIONS ON MEDICAL IMAGING,41(11),1-1.
MLA	Tai-Hsien Wu,et al."Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans".IEEE TRANSACTIONS ON MEDICAL IMAGING 41.11(2022):1-1.