Automatic Detection of Tooth-Gingiva Trim Lines on Dental Surfaces

Detecting the tooth-gingiva trim line from a dental surface plays a critical role in dental treatment planning and aligner 3D printing. Existing methods treat this task as a segmentation problem, which is resolved with geometric deep learning based mesh segmentation techniques. However, these method...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 42; H. 11; S. 1
Hauptverfasser:	Chen, Geng, Qin, Jie, Amor, Boulbaba Ben, Zhou, Weiming, Dai, Hang, Zhou, Tao, Huang, Heyuan, Shao, Ling
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.11.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Deep learning Dental Surface Finite element method Geometric Deep Learning Gingiva High resolution Image Processing, Computer-Assisted LDDMM Modules Printing, Three-Dimensional Segmentation Teeth Template Fitting Three dimensional printing Tooth-Gingiva Trim Line
ISSN:	0278-0062, 1558-254X, 1558-254X
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Abstract	Detecting the tooth-gingiva trim line from a dental surface plays a critical role in dental treatment planning and aligner 3D printing. Existing methods treat this task as a segmentation problem, which is resolved with geometric deep learning based mesh segmentation techniques. However, these methods can only provide indirect results (i.e., segmented teeth) and suffer from unsatisfactory accuracy due to the incapability of making full use of high-resolution dental surfaces. To this end, we propose a two-stage geometric deep learning framework for automatically detecting tooth-gingiva trim lines from dental surfaces. Our framework consists of a trim line proposal network (TLP-Net) for predicting an initial trim line from the low-resolution dental surface as well as a trim line refinement network (TLR-Net) for refining the initial trim line with the information from the high-resolution dental surface. Specifically, our TLP-Net predicts the initial trim line by fusing the multi-scale features from a U-Net with a proposed residual multi-scale attention fusion module. Moreover, we propose feature bridge modules and a trim line loss to further improve the accuracy. The resulting trim line is then fed to our TLR-Net, which is a deep-based LDDMM model with the high-resolution dental surface as input. In addition, dense connections are incorporated into TLR-Net for improved performance. Our framework provides an automatic solution to trim line detection by making full use of raw high-resolution dental surfaces. Extensive experiments on a clinical dental surface dataset demonstrate that our TLP-Net and TLR-Net are superior trim line detection methods and outperforms cutting-edge methods in both qualitative and quantitative evaluations.
AbstractList	Detecting the tooth-gingiva trim line from a dental surface plays a critical role in dental treatment planning and aligner 3D printing. Existing methods treat this task as a segmentation problem, which is resolved with geometric deep learning based mesh segmentation techniques. However, these methods can only provide indirect results (i.e., segmented teeth) and suffer from unsatisfactory accuracy due to the incapability of making full use of high-resolution dental surfaces. To this end, we propose a two-stage geometric deep learning framework for automatically detecting tooth-gingiva trim lines from dental surfaces. Our framework consists of a trim line proposal network (TLP-Net) for predicting an initial trim line from the low-resolution dental surface as well as a trim line refinement network (TLR-Net) for refining the initial trim line with the information from the high-resolution dental surface. Specifically, our TLP-Net predicts the initial trim line by fusing the multi-scale features from a U-Net with a proposed residual multi-scale attention fusion module. Moreover, we propose feature bridge modules and a trim line loss to further improve the accuracy. The resulting trim line is then fed to our TLR-Net, which is a deep-based LDDMM model with the high-resolution dental surface as input. In addition, dense connections are incorporated into TLR-Net for improved performance. Our framework provides an automatic solution to trim line detection by making full use of raw high-resolution dental surfaces. Extensive experiments on a clinical dental surface dataset demonstrate that our TLP-Net and TLR-Net are superior trim line detection methods and outperform cutting-edge methods in both qualitative and quantitative evaluations. Detecting the tooth-gingiva trim line from a dental surface plays a critical role in dental treatment planning and aligner 3D printing. Existing methods treat this task as a segmentation problem, which is resolved with geometric deep learning based mesh segmentation techniques. However, these methods can only provide indirect results (i.e., segmented teeth) and suffer from unsatisfactory accuracy due to the incapability of making full use of high-resolution dental surfaces. To this end, we propose a two-stage geometric deep learning framework for automatically detecting tooth-gingiva trim lines from dental surfaces. Our framework consists of a trim line proposal network (TLP-Net) for predicting an initial trim line from the low-resolution dental surface as well as a trim line refinement network (TLR-Net) for refining the initial trim line with the information from the high-resolution dental surface. Specifically, our TLP-Net predicts the initial trim line by fusing the multi-scale features from a U-Net with a proposed residual multi-scale attention fusion module. Moreover, we propose feature bridge modules and a trim line loss to further improve the accuracy. The resulting trim line is then fed to our TLR-Net, which is a deep-based LDDMM model with the high-resolution dental surface as input. In addition, dense connections are incorporated into TLR-Net for improved performance. Our framework provides an automatic solution to trim line detection by making full use of raw high-resolution dental surfaces. Extensive experiments on a clinical dental surface dataset demonstrate that our TLP-Net and TLR-Net are superior trim line detection methods and outperforms cutting-edge methods in both qualitative and quantitative evaluations. Detecting the tooth-gingiva trim line from a dental surface plays a critical role in dental treatment planning and aligner 3D printing. Existing methods treat this task as a segmentation problem, which is resolved with geometric deep learning based mesh segmentation techniques. However, these methods can only provide indirect results (i.e., segmented teeth) and suffer from unsatisfactory accuracy due to the incapability of making full use of high-resolution dental surfaces. To this end, we propose a two-stage geometric deep learning framework for automatically detecting tooth-gingiva trim lines from dental surfaces. Our framework consists of a trim line proposal network (TLP-Net) for predicting an initial trim line from the low-resolution dental surface as well as a trim line refinement network (TLR-Net) for refining the initial trim line with the information from the high-resolution dental surface. Specifically, our TLP-Net predicts the initial trim line by fusing the multi-scale features from a U-Net with a proposed residual multi-scale attention fusion module. Moreover, we propose feature bridge modules and a trim line loss to further improve the accuracy. The resulting trim line is then fed to our TLR-Net, which is a deep-based LDDMM model with the high-resolution dental surface as input. In addition, dense connections are incorporated into TLR-Net for improved performance. Our framework provides an automatic solution to trim line detection by making full use of raw high-resolution dental surfaces. Extensive experiments on a clinical dental surface dataset demonstrate that our TLP-Net and TLR-Net are superior trim line detection methods and outperform cutting-edge methods in both qualitative and quantitative evaluations.Detecting the tooth-gingiva trim line from a dental surface plays a critical role in dental treatment planning and aligner 3D printing. Existing methods treat this task as a segmentation problem, which is resolved with geometric deep learning based mesh segmentation techniques. However, these methods can only provide indirect results (i.e., segmented teeth) and suffer from unsatisfactory accuracy due to the incapability of making full use of high-resolution dental surfaces. To this end, we propose a two-stage geometric deep learning framework for automatically detecting tooth-gingiva trim lines from dental surfaces. Our framework consists of a trim line proposal network (TLP-Net) for predicting an initial trim line from the low-resolution dental surface as well as a trim line refinement network (TLR-Net) for refining the initial trim line with the information from the high-resolution dental surface. Specifically, our TLP-Net predicts the initial trim line by fusing the multi-scale features from a U-Net with a proposed residual multi-scale attention fusion module. Moreover, we propose feature bridge modules and a trim line loss to further improve the accuracy. The resulting trim line is then fed to our TLR-Net, which is a deep-based LDDMM model with the high-resolution dental surface as input. In addition, dense connections are incorporated into TLR-Net for improved performance. Our framework provides an automatic solution to trim line detection by making full use of raw high-resolution dental surfaces. Extensive experiments on a clinical dental surface dataset demonstrate that our TLP-Net and TLR-Net are superior trim line detection methods and outperform cutting-edge methods in both qualitative and quantitative evaluations.
Author	Qin, Jie Shao, Ling Zhou, Tao Amor, Boulbaba Ben Zhou, Weiming Chen, Geng Dai, Hang Huang, Heyuan
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References	ref13 chen (ref39) 2022 ref15 qi (ref41) 2017 ref14 wu (ref34) 2015 ref53 ref52 ref11 ref10 ref17 ref16 ref19 ref18 chen (ref29) 2018 ref51 ref46 ref45 ref48 ref47 ref42 ref44 qi (ref40) 2017 amor (ref43) 2021 ref49 ref7 ref9 ref4 ref3 ref6 ref5 ref35 ref36 ref31 ref30 ref33 ref32 ref2 ref1 gilmer (ref27) 2017 ref38 kingma (ref50) 2014 boscaini (ref37) 2016 lamecker (ref8) 2012 ref24 ref23 zanjani (ref12) 2019 ref25 ref22 ref21 ronneberger (ref20) 2015 ref28 li (ref26) 2016
References_xml	– ident: ref14 doi: 10.1007/978-3-030-32254-0_15 – year: 2021 ident: ref43 article-title: ResNet-LDDMM: Advancing the LDDMM framework using deep residual networks publication-title: arXiv 2102 07951 – ident: ref21 doi: 10.1007/s10278-019-00227-x – ident: ref9 doi: 10.1007/978-3-642-35428-1_30 – ident: ref19 doi: 10.1002/ima.22704 – ident: ref16 doi: 10.1007/978-3-030-59713-9_39 – ident: ref24 doi: 10.1109/ACCESS.2020.2975067 – ident: ref47 doi: 10.1145/2185520.2185525 – start-page: 557 year: 2019 ident: ref12 article-title: Deep learning approach to semantic segmentation in (3D) point cloud intra-oral scans of teeth publication-title: Proc Int Conf Med Imag Deep Learn – ident: ref18 doi: 10.1007/978-3-030-59719-1_68 – ident: ref5 doi: 10.1007/978-3-642-04271-3_117 – start-page: 1 year: 2016 ident: ref26 article-title: Gated graph sequence neural networks publication-title: Proc Int Conf Learn Represent – ident: ref48 doi: 10.1109/CVPR.2018.00262 – ident: ref2 doi: 10.1007/978-3-030-32226-7_93 – ident: ref3 doi: 10.1109/ICCP51029.2020.9266244 – ident: ref42 doi: 10.1109/CVPR.2019.00985 – ident: ref25 doi: 10.1109/TNN.2008.2005605 – ident: ref7 doi: 10.1016/j.compbiomed.2014.10.013 – ident: ref22 doi: 10.1145/3306346.3322959 – ident: ref30 doi: 10.1609/aaai.v32i1.11604 – ident: ref10 doi: 10.1007/978-3-030-00937-3_81 – ident: ref35 doi: 10.1109/ICCV.2015.114 – ident: ref53 doi: 10.3722/cadaps.2010.221-233 – start-page: 1263 year: 2017 ident: ref27 article-title: Neural message passing for quantum chemistry publication-title: Proc Int Conf Mach Learn – ident: ref36 doi: 10.1109/ICCVW.2015.112 – ident: ref44 doi: 10.1109/CVPR.2016.90 – ident: ref32 doi: 10.1007/978-3-030-59728-3_23 – ident: ref46 doi: 10.1016/j.media.2019.03.006 – ident: ref17 doi: 10.1016/j.gmod.2020.101071 – ident: ref45 doi: 10.1109/CVPR.2018.00745 – ident: ref1 doi: 10.1109/TMI.2004.824235 – start-page: 5099 year: 2017 ident: ref41 article-title: PointNet++: Deep hierarchical feature learning on point sets in a metric space publication-title: Proc Adv Neural Inf Process Syst – ident: ref49 doi: 10.1137/090766401 – ident: ref13 doi: 10.1109/ACCESS.2019.2924262 – ident: ref4 doi: 10.1179/1465313315Y.0000000001 – ident: ref33 doi: 10.1109/ISBI.2019.8759256 – year: 2014 ident: ref50 article-title: Adam: A method for stochastic optimization publication-title: arXiv 1412 6980 – ident: ref28 doi: 10.1609/aaai.v33i01.33013558 – start-page: 652 year: 2017 ident: ref40 article-title: PointNet: Deep learning on point sets for 3D classification and segmentation publication-title: Proc Conf Comput Vis Pattern Recognit – start-page: 3189 year: 2016 ident: ref37 article-title: Learning shape correspondence with anisotropic convolutional neural networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref11 doi: 10.1109/TVCG.2018.2839685 – ident: ref15 doi: 10.1109/TMI.2020.2971730 – start-page: 609 year: 2012 ident: ref8 article-title: Automatic detection and classification of teeth in CT data publication-title: Proc Int Conf Med Image Comput Comput -Assist Intervent – ident: ref52 doi: 10.1002/hbm.460030304 – ident: ref23 doi: 10.1109/MSP.2017.2693418 – ident: ref51 doi: 10.1016/j.ijrobp.2010.07.1062 – ident: ref38 doi: 10.1109/CVPR.2017.576 – year: 2022 ident: ref39 article-title: Automatic Schelling points detection from meshes publication-title: IEEE Trans Vis Comput Graphics – start-page: 1 year: 2018 ident: ref29 article-title: FastGCN: Fast learning with graph convolutional networks via importance sampling publication-title: Proc Int Conf Learn Represent – start-page: 234 year: 2015 ident: ref20 article-title: U-Net: Convolutional networks for biomedical image segmentation publication-title: Proc Int Conf Med Image Comput Comput -Assist Intervent – ident: ref6 doi: 10.1007/978-3-642-13681-8_61 – start-page: 1912 year: 2015 ident: ref34 article-title: 3D ShapeNets: A deep representation for volumetric shapes publication-title: Proc Conf Comput Vis Pattern Recognit – ident: ref31 doi: 10.1016/j.media.2020.101947
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Snippet	Detecting the tooth-gingiva trim line from a dental surface plays a critical role in dental treatment planning and aligner 3D printing. Existing methods treat...
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SubjectTerms	Deep learning Dental Surface Finite element method Geometric Deep Learning Gingiva High resolution Image Processing, Computer-Assisted LDDMM Modules Printing, Three-Dimensional Segmentation Teeth Template Fitting Three dimensional printing Tooth-Gingiva Trim Line
Title	Automatic Detection of Tooth-Gingiva Trim Lines on Dental Surfaces
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