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Li J, Cheng B, Niu N, Gao G, Ying S, Shi J, Zeng T. A fine-grained orthodontics segmentation model for 3D intraoral scan data. Comput Biol Med 2024; 168:107821. [PMID: 38064844 DOI: 10.1016/j.compbiomed.2023.107821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 11/01/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
With the widespread application of digital orthodontics in the diagnosis and treatment of oral diseases, more and more researchers focus on the accurate segmentation of teeth from intraoral scan data. The accuracy of the segmentation results will directly affect the follow-up diagnosis of dentists. Although the current research on tooth segmentation has achieved promising results, the 3D intraoral scan datasets they use are almost all indirect scans of plaster models, and only contain limited samples of abnormal teeth, so it is difficult to apply them to clinical scenarios under orthodontic treatment. The current issue is the lack of a unified and standardized dataset for analyzing and validating the effectiveness of tooth segmentation. In this work, we focus on deformed teeth segmentation and provide a fine-grained tooth segmentation dataset (3D-IOSSeg). The dataset consists of 3D intraoral scan data from more than 200 patients, with each sample labeled with a fine-grained mesh unit. Meanwhile, 3D-IOSSeg meticulously classified every tooth in the upper and lower jaws. In addition, we propose a fast graph convolutional network for 3D tooth segmentation named Fast-TGCN. In the model, the relationship between adjacent mesh cells is directly established by the naive adjacency matrix to better extract the local geometric features of the tooth. Extensive experiments show that Fast-TGCN can quickly and accurately segment teeth from the mouth with complex structures and outperforms other methods in various evaluation metrics. Moreover, we present the results of multiple classical tooth segmentation methods on this dataset, providing a comprehensive analysis of the field. All code and data will be available at https://github.com/MIVRC/Fast-TGCN.
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Affiliation(s)
- Juncheng Li
- School of Communication Information Engineering, Shanghai University, Shanghai, China.
| | - Bodong Cheng
- School of Computer Science and Technology, East China Normal University, Shanghai, China.
| | - Najun Niu
- School of Stomatology, Nanjing Medical University, Nanjing, China.
| | - Guangwei Gao
- Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China.
| | - Shihui Ying
- Department of Mathematics, School of Science, Shanghai University, Shanghai, China.
| | - Jun Shi
- School of Communication Information Engineering, Shanghai University, Shanghai, China.
| | - Tieyong Zeng
- Department of Mathematics, The Chinese University of Hong Kong, New Territories, Hong Kong.
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2
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Chen G, Qin J, Amor BB, Zhou W, Dai H, Zhou T, Huang H, Shao L. Automatic Detection of Tooth-Gingiva Trim Lines on Dental Surfaces. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3194-3204. [PMID: 37015112 DOI: 10.1109/tmi.2023.3263161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
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 outperform cutting-edge methods in both qualitative and quantitative evaluations.
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3
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Liu J, Hao J, Lin H, Pan W, Yang J, Feng Y, Wang G, Li J, Jin Z, Zhao Z, Liu Z. Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction. PATTERNS (NEW YORK, N.Y.) 2023; 4:100825. [PMID: 37720330 PMCID: PMC10499902 DOI: 10.1016/j.patter.2023.100825] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/24/2023] [Accepted: 07/21/2023] [Indexed: 09/19/2023]
Abstract
High-fidelity three-dimensional (3D) models of tooth-bone structures are valuable for virtual dental treatment planning; however, they require integrating data from cone-beam computed tomography (CBCT) and intraoral scans (IOS) using methods that are either error-prone or time-consuming. Hence, this study presents Deep Dental Multimodal Fusion (DDMF), an automatic multimodal framework that reconstructs 3D tooth-bone structures using CBCT and IOS. Specifically, the DDMF framework comprises CBCT and IOS segmentation modules as well as a multimodal reconstruction module with novel pixel representation learning architectures, prior knowledge-guided losses, and geometry-based 3D fusion techniques. Experiments on real-world large-scale datasets revealed that DDMF achieved superior segmentation performance on CBCT and IOS, achieving a 0.17 mm average symmetric surface distance (ASSD) for 3D fusion with a substantial processing time reduction. Additionally, clinical applicability studies have demonstrated DDMF's potential for accurately simulating tooth-bone structures throughout the orthodontic treatment process.
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Affiliation(s)
- Jiaxiang Liu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Hangzhou 310000, China
- Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining 314400, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
| | - Jin Hao
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
- Harvard School of Dental Medicine, Harvard University, Boston, MA 02115, USA
| | - Hangzheng Lin
- Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining 314400, China
| | - Wei Pan
- OPT Machine Vision Tech Co., Ltd., Tokyo 135-0064, Japan
| | - Jianfei Yang
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Yang Feng
- Angelalign Inc., Shanghai 200433, China
| | - Gaoang Wang
- Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining 314400, China
| | - Jin Li
- Department of Stomatology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen 518025, China
| | - Zuolin Jin
- Department of Orthodontics, School of Stomatology, Air Force Medical University, Xi’an 710032, China
| | - Zhihe Zhao
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Zuozhu Liu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Hangzhou 310000, China
- Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining 314400, China
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4
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Liu Z, He X, Wang H, Xiong H, Zhang Y, Wang G, Hao J, Feng Y, Zhu F, Hu H. Hierarchical Self-Supervised Learning for 3D Tooth Segmentation in Intra-Oral Mesh Scans. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:467-480. [PMID: 36378797 DOI: 10.1109/tmi.2022.3222388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Accurately delineating individual teeth and the gingiva in the three-dimension (3D) intraoral scanned (IOS) mesh data plays a pivotal role in many digital dental applications, e.g., orthodontics. Recent research shows that deep learning based methods can achieve promising results for 3D tooth segmentation, however, most of them rely on high-quality labeled dataset which is usually of small scales as annotating IOS meshes requires intensive human efforts. In this paper, we propose a novel self-supervised learning framework, named STSNet, to boost the performance of 3D tooth segmentation leveraging on large-scale unlabeled IOS data. The framework follows two-stage training, i.e., pre-training and fine-tuning. In pre-training, three hierarchical-level, i.e., point-level, region-level, cross-level, contrastive losses are proposed for unsupervised representation learning on a set of predefined matched points from different augmented views. The pretrained segmentation backbone is further fine-tuned in a supervised manner with a small number of labeled IOS meshes. With the same amount of annotated samples, our method can achieve an mIoU of 89.88%, significantly outperforming the supervised counterparts. The performance gain becomes more remarkable when only a small amount of labeled samples are available. Furthermore, STSNet can achieve better performance with only 40% of the annotated samples as compared to the fully supervised baselines. To the best of our knowledge, we present the first attempt of unsupervised pre-training for 3D tooth segmentation, demonstrating its strong potential in reducing human efforts for annotation and verification.
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Ma T, Yang Y, Zhai J, Yang J, Zhang J. A Tooth Segmentation Method Based on Multiple Geometric Feature Learning. Healthcare (Basel) 2022; 10:2089. [PMID: 36292536 PMCID: PMC9601705 DOI: 10.3390/healthcare10102089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/18/2022] [Indexed: 08/10/2023] Open
Abstract
Tooth segmentation is an important aspect of virtual orthodontic systems. In some existing studies using deep learning-based tooth segmentation methods, the feature learning of point coordinate information and normal vector information is not effectively distinguished. This will lead to the feature information of these two methods not producing complementary intermingling. To address this problem, a tooth segmentation method based on multiple geometric feature learning is proposed in this paper. First, the spatial transformation (T-Net) module is used to complete the alignment of dental model mesh features. Second, a multiple geometric feature learning module is designed to encode and enhance the centroid coordinates and normal vectors of each triangular mesh to highlight the differences between geometric features of different meshes. Finally, for local to global fusion features, feature downscaling and channel optimization are accomplished layer by layer using multilayer perceptron (MLP) and efficient channel attention (ECA). The experimental results show that our algorithm achieves better accuracy and efficiency of tooth segmentation and can assist dentists in their treatment work.
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6
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Efficient tooth gingival margin line reconstruction via adversarial learning. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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7
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Zhao Y, Zhang L, Liu Y, Meng D, Cui Z, Gao C, Gao X, Lian C, Shen D. Two-Stream Graph Convolutional Network for Intra-Oral Scanner Image Segmentation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:826-835. [PMID: 34714743 DOI: 10.1109/tmi.2021.3124217] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different raw attributes reveal completely different geometric information, the naive concatenation of different raw attributes at the (low-level) input stage may bring unnecessary confusion in describing and differentiating between mesh cells, thus hampering the learning of high-level geometric representations for the segmentation task. To address this issue, we design a two-stream graph convolutional network (i.e., TSGCN), which can effectively handle inter-view confusion between different raw attributes to more effectively fuse their complementary information and learn discriminative multi-view geometric representations. Specifically, our TSGCN adopts two input-specific graph-learning streams to extract complementary high-level geometric representations from coordinates and normal vectors, respectively. Then, these single-view representations are further fused by a self-attention module to adaptively balance the contributions of different views in learning more discriminative multi-view representations for accurate and fully automatic tooth segmentation. We have evaluated our TSGCN on a real-patient dataset of dental (mesh) models acquired by 3D intraoral scanners. Experimental results show that our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
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8
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Zhao Y, Zhang L, Yang C, Tan Y, Liu Y, Li P, Huang T, Gao C. 3D Dental model segmentation with graph attentional convolution network. Pattern Recognit Lett 2021. [DOI: 10.1016/j.patrec.2021.09.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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9
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Hao J, Liao W, Zhang YL, Peng J, Zhao Z, Chen Z, Zhou BW, Feng Y, Fang B, Liu ZZ, Zhao ZH. Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning. J Dent Res 2021; 101:304-311. [PMID: 34719980 DOI: 10.1177/00220345211040459] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Digital dentistry plays a pivotal role in dental health care. A critical step in many digital dental systems is to accurately delineate individual teeth and the gingiva in the 3-dimension intraoral scanned mesh data. However, previous state-of-the-art methods are either time-consuming or error prone, hence hindering their clinical applicability. This article presents an accurate, efficient, and fully automated deep learning model trained on a data set of 4,000 intraoral scanned data annotated by experienced human experts. On a holdout data set of 200 scans, our model achieves a per-face accuracy, average-area accuracy, and area under the receiver operating characteristic curve of 96.94%, 98.26%, and 0.9991, respectively, significantly outperforming the state-of-the-art baselines. In addition, our model takes only about 24 s to generate segmentation outputs, as opposed to >5 min by the baseline and 15 min by human experts. A clinical performance test of 500 patients with malocclusion and/or abnormal teeth shows that 96.9% of the segmentations are satisfactory for clinical applications, 2.9% automatically trigger alarms for human improvement, and only 0.2% of them need rework. Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry.
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Affiliation(s)
- J Hao
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and West China Hospital of Stomatology, Sichuan University, Chengdu, China.,Harvard School of Dental Medicine, Harvard University, Boston, MA, USA
| | - W Liao
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Y L Zhang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - J Peng
- DeepAlign Tech Inc., Ningbo, China
| | - Z Zhao
- DeepAlign Tech Inc., Ningbo, China
| | - Z Chen
- DeepAlign Tech Inc., Ningbo, China
| | - B W Zhou
- Angelalign Research Institute, Angel Align Inc., Shanghai, China
| | - Y Feng
- Angelalign Research Institute, Angel Align Inc., Shanghai, China
| | - B Fang
- Ninth People's Hospital Affiliated to Shanghai Jiao Tong University, Shanghai Research Institute of Stomatology, National Clinical Research Center of Stomatology, Shanghai, China
| | - Z Z Liu
- Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China
| | - Z H Zhao
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and West China Hospital of Stomatology, Sichuan University, Chengdu, China
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10
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Zanjani FG, Pourtaherian A, Zinger S, Moin DA, Claessen F, Cherici T, Parinussa S, de With PH. Mask-MCNet: Tooth instance segmentation in 3D point clouds of intra-oral scans. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.06.145] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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11
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Bae M, Park JW, Kim N. Fully automated estimation of arch forms in cone-beam CT with cubic B-spline approximation: Evaluation of digital dental models with missing teeth. Comput Biol Med 2021; 131:104256. [PMID: 33610000 DOI: 10.1016/j.compbiomed.2021.104256] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 01/18/2021] [Accepted: 02/03/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND AND OBJECTIVE To evaluate the automatic determination method for the arch form in cone-beam computed tomography (CBCT) images with cubic B-spline approximation on digital dental models using various types of missing teeth. METHODS The maxilla and mandible from eight dental CBCT images with Class I occlusion and no missing teeth were used in this study. The dental arch determination algorithm using cubic B-spline approximation was modified by applying a smoothing function for reliable curve fitting to the digital dental models with various types of missing teeth. For evaluation, 31 scenarios with missing teeth were simulated, and cases with 1-8 missing teeth were divided into three groups: solitary, consecutive, and multiple (more than 4) missing teeth. The prediction accuracies of the dental arch forms were evaluated through comparisons with the gold standards for the digital dental models by two expert orthodontists. RESULTS The distance errors between the gold standards and the estimated results of the dental arch forms in all types of models were 0.237-1.740 mm. The mean distance errors of the solitary, consecutive, and multiple groups were 0.436 ± 0.124 mm (0.237-0.964 mm), 0.591 ± 0.250 mm (0.256-1.482 mm), and 0.679 ± 0.310 mm (0.254-1.740 mm), respectively. CONCLUSIONS The algorithm for predicting the arch form functioned reliably, even for digital dental models with various types of missing teeth, and could be applied to digital dentistry for applications such as orthodontic tooth setup, artificial tooth arrangement for denture fabrication, and implant guides.
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Affiliation(s)
- Myungsoo Bae
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Jae-Woo Park
- Department of Orthodontics, Kooalldam Dental Hospital, 1418 Gyeongwon-daero, Bupyeong-gu, Incheon, Republic of Korea.
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul, 05505, Republic of Korea; Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul, 05505, Republic of Korea.
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12
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Yang Y, Xie R, Jia W, Chen Z, Yang Y, Xie L, Jiang B. Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.07.110] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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13
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Cui Z, Li C, Chen N, Wei G, Chen R, Zhou Y, Shen D, Wang W. TSegNet: An efficient and accurate tooth segmentation network on 3D dental model. Med Image Anal 2020; 69:101949. [PMID: 33387908 DOI: 10.1016/j.media.2020.101949] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 11/06/2020] [Accepted: 12/12/2020] [Indexed: 11/26/2022]
Abstract
Automatic and accurate segmentation of dental models is a fundamental task in computer-aided dentistry. Previous methods can achieve satisfactory segmentation results on normal dental models; however, they fail to robustly handle challenging clinical cases such as dental models with missing, crowding, or misaligned teeth before orthodontic treatments. In this paper, we propose a novel end-to-end learning-based method, called TSegNet, for robust and efficient tooth segmentation on 3D scanned point cloud data of dental models. Our algorithm detects all the teeth using a distance-aware tooth centroid voting scheme in the first stage, which ensures the accurate localization of tooth objects even with irregular positions on abnormal dental models. Then, a confidence-aware cascade segmentation module in the second stage is designed to segment each individual tooth and resolve ambiguities caused by aforementioned challenging cases. We evaluated our method on a large-scale real-world dataset consisting of dental models scanned before or after orthodontic treatments. Extensive evaluations, ablation studies and comparisons demonstrate that our method can generate accurate tooth labels robustly in various challenging cases and significantly outperforms state-of-the-art approaches by 6.5% of Dice Coefficient, 3.0% of F1 score in term of accuracy, while achieving 20 times speedup of computational time.
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Affiliation(s)
- Zhiming Cui
- Department of Computer Science, The University of Hong Kong, Hong Kong, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Changjian Li
- Department of Computer Science, University College London, London, UK; Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Nenglun Chen
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Guodong Wei
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Runnan Chen
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Yuanfeng Zhou
- Department of Software Engineering, Shandong University, Jinan, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea.
| | - Wenping Wang
- Department of Computer Science, The University of Hong Kong, Hong Kong, China.
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14
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Yuan T, Wang Y, Hou Z, Wang J. Tooth segmentation and gingival tissue deformation framework for 3D orthodontic treatment planning and evaluating. Med Biol Eng Comput 2020; 58:2271-2290. [PMID: 32700290 DOI: 10.1007/s11517-020-02230-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 07/08/2020] [Indexed: 11/29/2022]
Abstract
In this study, we propose an integrated tooth segmentation and gingival tissue deformation simulation framework used to design and evaluate the orthodontic treatment plan especially with invisible aligners. Firstly, the bio-characteristics information of the digital impression is analyzed quantitatively and demonstrated visually. With the derived information, the transitional regions of tooth-tooth and tooth-gingiva are extracted as the solution domain of the segmentation boundaries. Then, a boundary detection approach is proposed, which is used for the tooth segmentation and region division of the digital impression. After tooth segmentation, we propose the deformation simulation framework driven by energy function based on the biological deformation properties of gingival tissues. The correctness and availability of the proposed segmentation and gingival tissue deformation simulation framework are demonstrated with typical cases and qualitative analysis. Experimental results show that segmentation boundaries calculated by the proposed method are accurate, and local details of the digital impression under study are preserved well during deformation simulation. Qualitative analysis results of the gingival tissues' surface area and volume variations indicate that the proposed gingival tissue deformation simulation framework is consistent with the clinical gingival tissue deformation characteristics, and it can be used to predict the rationality of the treatment plan from both visual inspection and numerical simulation. The proposed tooth segmentation and gingival tissue deformation simulation framework is shown to be effective and has good practicability, but accurate quantitative analysis based on clinical results is still an open problem in this study. Combined with tooth rearrangement steps, it can be used to design the orthodontic treatment plan, and to output the data for production of invisible aligners. Graphical abstract.
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Affiliation(s)
- Tianran Yuan
- Huaiyin Institute of Technology, Huai'an, China. .,Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Yimin Wang
- Huaiyin Institute of Technology, Huai'an, China
| | - Zhiwei Hou
- Huaiyin Institute of Technology, Huai'an, China
| | - Jun Wang
- Nanjing University of Aeronautics and Astronautics, Nanjing, China
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15
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Lian C, Wang L, Wu TH, Wang F, Yap PT, Ko CC, Shen D. Deep Multi-Scale Mesh Feature Learning for Automated Labeling of Raw Dental Surfaces From 3D Intraoral Scanners. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2440-2450. [PMID: 32031933 DOI: 10.1109/tmi.2020.2971730] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Precisely labeling teeth on digitalized 3D dental surface models is the precondition for tooth position rearrangements in orthodontic treatment planning. However, it is a challenging task primarily due to the abnormal and varying appearance of patients' teeth. The emerging utilization of intraoral scanners (IOSs) in clinics further increases the difficulty in automated tooth labeling, as the raw surfaces acquired by IOS are typically low-quality at gingival and deep intraoral regions. In recent years, some pioneering end-to-end methods (e.g., PointNet) have been proposed in the communities of computer vision and graphics to consume directly raw surface for 3D shape segmentation. Although these methods are potentially applicable to our task, most of them fail to capture fine-grained local geometric context that is critical to the identification of small teeth with varying shapes and appearances. In this paper, we propose an end-to-end deep-learning method, called MeshSegNet, for automated tooth labeling on raw dental surfaces. Using multiple raw surface attributes as inputs, MeshSegNet integrates a series of graph-constrained learning modules along its forward path to hierarchically extract multi-scale local contextual features. Then, a dense fusion strategy is applied to combine local-to-global geometric features for the learning of higher-level features for mesh cell annotation. The predictions produced by our MeshSegNet are further post-processed by a graph-cut refinement step for final segmentation. We evaluated MeshSegNet using a real-patient dataset consisting of raw maxillary surfaces acquired by 3D IOS. Experimental results, performed 5-fold cross-validation, demonstrate that MeshSegNet significantly outperforms state-of-the-art deep learning methods for 3D shape segmentation.
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16
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Tsuji M, Suzuki H, Suzuki S, Moriyama K. Three-dimensional evaluation of morphology and position of impacted supernumerary teeth in cases of cleidocranial dysplasia. Congenit Anom (Kyoto) 2020; 60:106-114. [PMID: 31599034 PMCID: PMC7383483 DOI: 10.1111/cga.12358] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 09/13/2019] [Accepted: 10/02/2019] [Indexed: 12/19/2022]
Abstract
Cleidocranial dysplasia (CCD) is a congenital anomaly characterized by the presence of impacted supernumerary teeth and delayed eruption of permanent teeth. However, there has been no detailed investigation on supernumerary teeth in patients with CCD using three-dimensional (3D) imaging techniques. The purpose of this study was to elucidate the morphology and position of supernumerary teeth using 3D images reconstructed from cone-beam computed tomography (CBCT) data in a group of five Japanese subjects (male, 3; female, 2; age, 15.0-25.4 years) with CCD. All five subjects exhibited supernumerary teeth (39 in total; average, 7.8; range, 1-15). All supernumerary teeth were impacted and existed as pairs with adjacent permanent teeth. Comparison of the size (the crown and dental-root lengths, the crown mesiodistal and buccolingual diameters), the number of cusps and dental roots, the position, and direction of supernumerary teeth in relation to the adjacent permanent teeth was analyzed. The results of relationship analyses revealed that, at sites other than the molar region, supernumerary teeth were positioned on the lingual and distal sides and supernumerary teeth resembled the morphology of their adjacent permanent teeth in terms of the number of cusps but were smaller than the adjacent permanent teeth. In the molar region, supernumerary teeth were microdontia, which were apparently small and obscure morphologically. In addition, while all adjacent permanent teeth exhibited normal direction, five supernumerary teeth exhibited inverse direction. The findings of this study will improve our understanding of the characteristics of CCD and provide important information for the pathophysiology and clinical treatment.
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Affiliation(s)
- Michiko Tsuji
- Maxillofacial Orthognathics, Department of Maxillofacial Reconstruction and Function, Division of Maxillofacial/Neck Reconstruction, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hiroyuki Suzuki
- Maxillofacial Orthognathics, Department of Maxillofacial Reconstruction and Function, Division of Maxillofacial/Neck Reconstruction, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Shoichi Suzuki
- Maxillofacial Orthognathics, Department of Maxillofacial Reconstruction and Function, Division of Maxillofacial/Neck Reconstruction, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Keiji Moriyama
- Maxillofacial Orthognathics, Department of Maxillofacial Reconstruction and Function, Division of Maxillofacial/Neck Reconstruction, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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Automated Identification from Dental Data (AutoIDD): A new development in digital forensics. Forensic Sci Int 2020; 309:110218. [DOI: 10.1016/j.forsciint.2020.110218] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 02/14/2020] [Accepted: 02/20/2020] [Indexed: 11/23/2022]
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Geetha V, Aprameya KS, Hinduja DM. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Inf Sci Syst 2020; 8:8. [PMID: 31949895 DOI: 10.1007/s13755-019-0096-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 12/21/2019] [Indexed: 10/25/2022] Open
Abstract
Purpose An algorithm for diagnostic system with neural network is developed for diagnosis of dental caries in digital radiographs. The diagnostic performance of the designed system is evaluated. Methods The diagnostic system comprises of Laplacian filtering, window based adaptive threshold, morphological operations, statistical feature extraction and back-propagation neural network. The back propagation neural network used to classify a tooth surface as normal or having dental caries. The 105 images derived from intra-oral digital radiography, are used to train an artificial neural network with 10-fold cross validation. The caries in these dental radiographs are annotated by a dentist. The performance of the diagnostic algorithm is evaluated and compared with baseline methods. Results The system gives an accuracy of 97.1%, false positive (FP) rate of 2.8%, receiver operating characteristic (ROC) area of 0.987 and precision recall curve (PRC) area of 0.987 with learning rate of 0.4, momentum of 0.2 and 500 iterations with single hidden layer with 9 nodes. Conclusions This study suggests that dental caries can be predicted more accurately with back-propagation neural network. There is a need for improving the system for classification of caries depth. More improved algorithms and high quantity and high quality datasets may give still better tooth decay detection in clinical dental practice.
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Affiliation(s)
- V Geetha
- 1Department of Electronics and Communication Engineering, University BDT College of Engineering, Davanagere, Karnataka 577004 India
| | - K S Aprameya
- 2Department of Electrical and Electronics Engineering, University BDT College of Engineering, Davanagere, Karnataka 577004 India
| | - Dharam M Hinduja
- Department of Conservative Dentistry and Endodontics, S.J.M. Dental College & Hospital, Chitradurga, Karnataka 577501 India
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Xu X, Liu C, Zheng Y. 3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2019; 25:2336-2348. [PMID: 29994311 DOI: 10.1109/tvcg.2018.2839685] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In this paper, we present a novel approach for 3D dental model segmentation via deep Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to receive undesirable results due to the complex appearance of human teeth (e.g., missing/rotten teeth, feature-less regions, crowding teeth, extra medical attachments, etc.). Furthermore, labeling of individual tooth is hardly enabled in traditional tooth segmentation methods. To address these issues, we propose to learn a generic and robust segmentation model by exploiting deep Neural Networks, namely NNs. The segmentation task is achieved by labeling each mesh face. We extract a set of geometry features as face feature representations. In the training step, the network is fed with those features, and produces a probability vector, of which each element indicates the probability a face belonging to the corresponding model part. To this end, we extensively experiment with various network structures, and eventually arrive at a 2-level hierarchical CNNs structure for tooth segmentation: one for teeth-gingiva labeling and the other for inter-teeth labeling. Further, we propose a novel boundary-aware tooth simplification method to significantly improve efficiency in the stage of feature extraction. After CNNs prediction, we do graph-based label optimization and further refine the boundary with an improved version of fuzzy clustering. The accuracy of our mesh labeling method exceeds that of the state-of-art geometry-based methods, reaching 99.06 percent measured by area which is directly applicable in orthodontic CAD systems. It is also robust to any possible foreign matters on model surface, e.g., air bubbles, dental accessories, and many more.
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Bae M, Park JW, Kim N. Semi-automatic and robust determination of dental arch form in dental cone-beam CT with B-spline approximation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 172:95-101. [PMID: 30902131 DOI: 10.1016/j.cmpb.2019.02.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 02/09/2019] [Accepted: 02/23/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE The dental arch form is generally used as a base for planning orthodontic treatments. It is, therefore, vital to determine the proper individual dental arch form for more accurate orthodontic treatment. We aimed to develop and validate a robust algorithm for semi-automatic determination of the dental arch form in dental cone-beam CT (CBCT) images with the cubic B-spline approximation. METHODS Our algorithm consists of tooth segmentation, determination of an occlusal plane, and generation of intersection points between the teeth and the offset plane from the occlusal plane in CBCT images. By fitting a curve to the intersection points using the cubic B-spline curve approximation, the dental arch form was finally determined. The accuracy of the dental arch forms was evaluated by comparison with gold standards determined by an expert orthodontist. RESULTS Thirteen dental CBCT scans from nine subjects were enrolled in this study. From the CBCT scans, 13 maxillary arch forms and 11 mandibular arch forms with Class I occlusion were determined by our proposed algorithm and evaluated for validation. The mean error between the dental arch forms of gold standards and our method using the cubic B-spline was 0.413 ± 0.092 mm (range, 0.264-0.587 mm). CONCLUSIONS Our proposed method showed reliable accuracy of determining the dental arch forms for the maxilla and mandible. These results suggested that this method might be used for planning automatic tooth setup for individual patients.
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Affiliation(s)
- Myungsoo Bae
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, Republic of Korea
| | - Jae-Woo Park
- Department of Orthodontics, Kooalldam Dental Hospital, 1418 Gyeongwon-daero, Bupyeong-gu, Incheon, Republic of Korea.
| | - Namkug Kim
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, Republic of Korea; Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, Republic of Korea.
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Destrez R, Albouy-Kissi B, Treuillet S, Lucas Y. Automatic registration of 3D dental mesh based on photographs of patient’s mouth. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2018. [DOI: 10.1080/21681163.2018.1519849] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
| | - Benjamin Albouy-Kissi
- Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, Clermont-Ferrand, France
| | | | - Yves Lucas
- Laboratoire PRISME, Université d’Orléans, Orléans, France
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Kim S, Choi S. Automatic tooth segmentation of dental mesh using a transverse plane. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:4122-4125. [PMID: 30441262 DOI: 10.1109/embc.2018.8513318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper proposes an automatic method to separate the gingiva and individual teeth from a dental mesh. We define a transverse plane that produces a cross-section of tooth lingual and labial surfaces, preserving the shape of individual teeth. The upper vertices from the transverse plane, which belong to the tooth, are projected onto the transverse plane, and partitioned into individual teeth. We apply region growing to the remaining non-segmented parts to determine the cluster the vertices belong to, and the proposed approach is fully automatic, i.e., segmentation does not require user interaction for feature point search or tooth boundary markers. The proposed segmentation method is applied to several dental mesh models to demonstrate its robustness.
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A hemispherical contact model for simplifying 3D occlusal surfaces. J Prosthet Dent 2017; 119:804-811. [PMID: 28967402 DOI: 10.1016/j.prosdent.2017.06.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Revised: 06/23/2017] [Accepted: 06/23/2017] [Indexed: 11/20/2022]
Abstract
STATEMENT OF PROBLEM Currently, dental articulators can recreate mandibular movements and occlusal contacts. However, whether virtual articulators can also provide information about occluding dental surfaces, functional movements, and the mandibular condyles is unclear. PURPOSE The purpose of this in vitro study was to evaluate the occluding surfaces on dental casts obtained from a patient and approximate them to a hemispherical contact model. Both models were tested by digitizing the Dentatus ARL dental articulator. MATERIAL AND METHODS A combination of photogrammetry and structure from motion methods were used to scan a Dentatus ARL articulator and representative dental casts. Using computer-aided engineering and finite element analysis, contact points and action vectors to the forces on occluding surfaces and condyles were obtained for cast and hemispherical models. This experiment was performed using centric occlusion and 3 different condylar inclinations. The Kruskal-Wallis 1-way analysis of variance on ranks test was used to allow all pairwise comparisons between condylar inclination and mechanical action vector values in each location (α=.05). RESULTS Action vectors from the cast model and each location of the hemispherical model were calculated to show the mechanical consequences and the similarity among models. Overall, no significant differences were observed for action vectors (A20 versus A40 versus A60) at each location (dental cast/hemisphere, right condylar, and left condylar) in the analysis of dental casts and the hemisphere model (.382≤P≤.999). CONCLUSIONS This study provided graphical information that may assist the dental professional in determining which occlusal contacts should be modified to attain condylar and balanced centric occlusion.
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Raith S, Vogel EP, Anees N, Keul C, Güth JF, Edelhoff D, Fischer H. Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data. Comput Biol Med 2017; 80:65-76. [DOI: 10.1016/j.compbiomed.2016.11.013] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 11/17/2016] [Accepted: 11/26/2016] [Indexed: 11/26/2022]
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Li Z, Wang H. Interactive Tooth Separation from Dental Model Using Segmentation Field. PLoS One 2016; 11:e0161159. [PMID: 27532266 PMCID: PMC4988775 DOI: 10.1371/journal.pone.0161159] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Accepted: 08/01/2016] [Indexed: 11/18/2022] Open
Abstract
Tooth segmentation on dental model is an essential step of computer-aided-design systems for orthodontic virtual treatment planning. However, fast and accurate identifying cutting boundary to separate teeth from dental model still remains a challenge, due to various geometrical shapes of teeth, complex tooth arrangements, different dental model qualities, and varying degrees of crowding problems. Most segmentation approaches presented before are not able to achieve a balance between fine segmentation results and simple operating procedures with less time consumption. In this article, we present a novel, effective and efficient framework that achieves tooth segmentation based on a segmentation field, which is solved by a linear system defined by a discrete Laplace-Beltrami operator with Dirichlet boundary conditions. A set of contour lines are sampled from the smooth scalar field, and candidate cutting boundaries can be detected from concave regions with large variations of field data. The sensitivity to concave seams of the segmentation field facilitates effective tooth partition, as well as avoids obtaining appropriate curvature threshold value, which is unreliable in some case. Our tooth segmentation algorithm is robust to dental models with low quality, as well as is effective to dental models with different levels of crowding problems. The experiments, including segmentation tests of varying dental models with different complexity, experiments on dental meshes with different modeling resolutions and surface noises and comparison between our method and the morphologic skeleton segmentation method are conducted, thus demonstrating the effectiveness of our method.
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Affiliation(s)
- Zhongyi Li
- School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Hao Wang
- School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
- * E-mail:
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A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation. APPL INTELL 2016. [DOI: 10.1007/s10489-016-0763-5] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Okada K, Rysavy S, Flores A, Linguraru MG. Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans. Med Phys 2015; 42:1653-65. [PMID: 25832055 DOI: 10.1118/1.4914418] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
PURPOSE This paper proposes a novel application of computer-aided diagnosis (CAD) to an everyday clinical dental challenge: the noninvasive differential diagnosis of periapical lesions between periapical cysts and granulomas. A histological biopsy is the most reliable method currently available for this differential diagnosis; however, this invasive procedure prevents the lesions from healing noninvasively despite a report that they may heal without surgical treatment. A CAD using cone-beam computed tomography (CBCT) offers an alternative noninvasive diagnostic tool which helps to avoid potentially unnecessary surgery and to investigate the unknown healing process and rate for the lesions. METHODS The proposed semiautomatic solution combines graph-based random walks segmentation with machine learning-based boosted classifiers and offers a robust clinical tool with minimal user interaction. As part of this CAD framework, the authors provide two novel technical contributions: (1) probabilistic extension of the random walks segmentation with likelihood ratio test and (2) LDA-AdaBoost: a new integration of weighted linear discriminant analysis to AdaBoost. RESULTS A dataset of 28 CBCT scans is used to validate the approach and compare it with other popular segmentation and classification methods. The results show the effectiveness of the proposed method with 94.1% correct classification rate and an improvement of the performance by comparison with the Simon's state-of-the-art method by 17.6%. The authors also compare classification performances with two independent ground-truth sets from the histopathology and CBCT diagnoses provided by endodontic experts. CONCLUSIONS Experimental results of the authors show that the proposed CAD system behaves in clearer agreement with the CBCT ground-truth than with histopathology, supporting the Simon's conjecture that CBCT diagnosis can be as accurate as histopathology for differentiating the periapical lesions.
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Affiliation(s)
- Kazunori Okada
- Department of Computer Science, San Francisco State University, San Francisco, California 94132
| | - Steven Rysavy
- Biomedical and Health Informatics Program, University of Washington, Seattle, Washington 98195
| | - Arturo Flores
- Computer Science and Engineering, University of California, San Diego, California 92093
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC 20010 and Departments of Radiology and Pediatrics, George Washington University, Washington, DC 20037
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Zhang J, Xia JJ, Li J, Zhou X. Reconstruction-Based Digital Dental Occlusion of the Partially Edentulous Dentition. IEEE J Biomed Health Inform 2015; 21:201-210. [PMID: 26584502 DOI: 10.1109/jbhi.2015.2500191] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Partially edentulous dentition presents a challenging problem for the surgical planning of digital dental occlusion in the field of craniomaxillofacial surgery because of the incorrect maxillomandibular distance caused by missing teeth. We propose an innovative approach called Dental Reconstruction with Symmetrical Teeth (DRST) to achieve accurate dental occlusion for the partially edentulous cases. In this DRST approach, the rigid transformation between two symmetrical teeth existing on the left and right dental model is estimated through probabilistic point registration by matching the two shapes. With the estimated transformation, the partially edentulous space can be virtually filled with the teeth in its symmetrical position. Dental alignment is performed by digital dental occlusion reestablishment algorithm with the reconstructed complete dental model. Satisfactory reconstruction and occlusion results are demonstrated with the synthetic and real partially edentulous models.
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Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields. BIOMED RESEARCH INTERNATIONAL 2015; 2015:187173. [PMID: 26413507 PMCID: PMC4564592 DOI: 10.1155/2015/187173] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2014] [Accepted: 01/06/2015] [Indexed: 11/18/2022]
Abstract
An important preprocess in computer-aided orthodontics is to segment teeth from the dental models accurately, which should involve manual interactions as few as possible. But fully automatic partition of all teeth is not a trivial task, since teeth occur in different shapes and their arrangements vary substantially from one individual to another. The difficulty is exacerbated when severe teeth malocclusion and crowding problems occur, which is a common occurrence in clinical cases. Most published methods in this area either are inaccurate or require lots of manual interactions. Motivated by the state-of-the-art general mesh segmentation methods that adopted the theory of harmonic field to detect partition boundaries, this paper proposes a novel, dental-targeted segmentation framework for dental meshes. With a specially designed weighting scheme and a strategy of a priori knowledge to guide the assignment of harmonic constraints, this method can identify teeth partition boundaries effectively. Extensive experiments and quantitative analysis demonstrate that the proposed method is able to partition high-quality teeth automatically with robustness and efficiency.
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Fast and Accurate Semiautomatic Segmentation of Individual Teeth from Dental CT Images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:810796. [PMID: 26413143 PMCID: PMC4564792 DOI: 10.1155/2015/810796] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Revised: 08/03/2015] [Accepted: 08/05/2015] [Indexed: 11/29/2022]
Abstract
DIn this paper, we propose a fast and accurate semiautomatic method to effectively distinguish individual teeth from the sockets of teeth in dental CT images. Parameter values of thresholding and shapes of the teeth are propagated to the neighboring slice, based on the separated teeth from reference images. After the propagation of threshold values and shapes of the teeth, the histogram of the current slice was analyzed. The individual teeth are automatically separated and segmented by using seeded region growing. Then, the newly generated separation information is iteratively propagated to the neighboring slice. Our method was validated by ten sets of dental CT scans, and the results were compared with the manually segmented result and conventional methods. The average error of absolute value of volume measurement was 2.29 ± 0.56%, which was more accurate than conventional methods. Boosting up the speed with the multicore processors was shown to be 2.4 times faster than a single core processor. The proposed method identified the individual teeth accurately, demonstrating that it can give dentists substantial assistance during dental surgery.
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Geometrical modeling of complete dental shapes by using panoramic X-ray, digital mouth data and anatomical templates. Comput Med Imaging Graph 2015; 43:112-21. [DOI: 10.1016/j.compmedimag.2015.01.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2014] [Revised: 01/08/2015] [Accepted: 01/09/2015] [Indexed: 11/22/2022]
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Zou BJ, Liu SJ, Liao SH, Ding X, Liang Y. Interactive tooth partition of dental mesh base on tooth-target harmonic field. Comput Biol Med 2014; 56:132-44. [PMID: 25464355 DOI: 10.1016/j.compbiomed.2014.10.013] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2014] [Revised: 10/01/2014] [Accepted: 10/11/2014] [Indexed: 10/24/2022]
Abstract
The accurate tooth partition of dental mesh is a crucial step in computer-aided orthodontics. However, tooth boundary identification is not a trivial task for tooth partition, since different shapes and their arrangements vary substantially among common clinical cases. Though curvature field is traditionally used for identifying boundaries, it is normally not reliable enough. Other methods may improve the accuracy, but require intensive user interaction. Motivated by state-of-the-art general interactive mesh segmentation methods, this paper proposes a novel tooth-target partition framework that employs harmonic fields to partition teeth accurately and effectively. In addition, a refining strategy is introduced to successfully segment teeth from the complicated dental model with indistinctive tooth boundaries on its lingual side surface, addressing an issue that had not been solved properly before. To utilise high-level information provided by the user, smart and intuitive user interfaces are also proposed with minimum interaction. In fact, most published interactive methods specifically designed for tooth partition are lacking efficient user interfaces. Extensive experiments and quantitative analyses show that our tooth partition method outperforms the state-of-the-art approaches in terms of accuracy, robustness and efficiency.
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Affiliation(s)
- Bei-ji Zou
- School of Information Science and Engineering, Central South University, Changsha, PR China.
| | - Shi-jian Liu
- School of Information Science and Engineering, Central South University, Changsha, PR China.
| | - Sheng-hui Liao
- School of Information Science and Engineering, Central South University, Changsha, PR China.
| | - Xi Ding
- Department of Stomatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China
| | - Ye Liang
- Department of Stomatology, Xiangya Hospital of Central South University, Changsha, PR China
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Kašparová M, Procházka A, Grajciarová L, Yadollahi M, Vyšata O, Dostálová T. Evaluation of dental morphometrics during the orthodontic treatment. Biomed Eng Online 2014; 13:68. [PMID: 24893983 PMCID: PMC4058703 DOI: 10.1186/1475-925x-13-68] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2014] [Accepted: 05/23/2014] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Diagnostic orthodontic and prosthetic procedures commence with an initial examination, during which a number of individual findings on occlusion or malocclusion are clarified. Nowadays we try to replace standard plaster casts by scanned objects and digital models. METHOD Geometrically calibrated images aid in the comparison of several different steps of the treatment and show the variation of selected features belonging to individual biomedical objects. The methods used are based on geometric morphometrics, making a new approach to the evaluation of the variability of features. The study presents two different methods of measurement and shows their accuracy and reliability. RESULTS The experimental part of the present paper is devoted to the analysis of the dental arch objects of 24 patients before and after the treatment using the distances between the canines and premolars as the features important for diagnostic purposes. Our work proved the advantage of measuring digitalized orthodontic models over manual measuring of plaster casts, with statistically significant results and accuracy sufficient for dental practice. CONCLUSION A new method of computer imaging and measurements of a dental stone cast provides information with the precision required for orthodontic treatment. The results obtained point to the reduction in the variance of the distances between the premolars and canines during the treatment, with a regression coefficient RC=0.7 and confidence intervals close enough for dental practice. The ratio of these distances pointed to the nearly constant value of this measure close to 0.84 for the given set of 24 individuals.
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Affiliation(s)
- Magdaléna Kašparová
- Department of Paediatric Stomatology, The Second Medical Faculty, Charles University, V Úvalu 84, 150 06 Prague 5, Czech Republic
| | - Aleš Procházka
- Department of Computing and Control Engineering, Institute of Chemical Technology in Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Lucie Grajciarová
- Department of Computing and Control Engineering, Institute of Chemical Technology in Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Mohammadreza Yadollahi
- Department of Computing and Control Engineering, Institute of Chemical Technology in Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Oldřich Vyšata
- Department of Neurology, Charles University, Sokolská 581, 500 05 Hradec Králové, Czech Republic
| | - Tat’jana Dostálová
- Department of Paediatric Stomatology, The Second Medical Faculty, Charles University, V Úvalu 84, 150 06 Prague 5, Czech Republic
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Zhong X, Yu D, Wong YS, Sim T, Lu WF, Foong KWC, Cheng HL. 3D dental biometrics: Alignment and matching of dental casts for human identification. COMPUT IND 2013. [DOI: 10.1016/j.compind.2013.06.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Kasparova M, Grafova L, Dvorak P, Dostalova T, Prochazka A, Eliasova H, Prusa J, Kakawand S. Possibility of reconstruction of dental plaster cast from 3D digital study models. Biomed Eng Online 2013; 12:49. [PMID: 23721330 PMCID: PMC3686614 DOI: 10.1186/1475-925x-12-49] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2013] [Accepted: 05/22/2013] [Indexed: 11/16/2022] Open
Abstract
Objectives To compare traditional plaster casts, digital models and 3D printed copies of dental plaster casts based on various criteria. To determine whether 3D printed copies obtained using open source system RepRap can replace traditional plaster casts in dental practice. To compare and contrast the qualities of two possible 3D printing options – open source system RepRap and commercially available 3D printing. Design and settings A method comparison study on 10 dental plaster casts from the Orthodontic department, Department of Stomatology, 2nd medical Faulty, Charles University Prague, Czech Republic. Material and methods Each of 10 plaster casts were scanned by inEos Blue scanner and the printed on 3D printer RepRap [10 models] and ProJet HD3000 3D printer [1 model]. Linear measurements between selected points on the dental arches of upper and lower jaws on plaster casts and its 3D copy were recorded and statistically analyzed. Results 3D printed copies have many advantages over traditional plaster casts. The precision and accuracy of the RepRap 3D printed copies of plaster casts were confirmed based on the statistical analysis. Although the commercially available 3D printing enables to print more details than the RepRap system, it is expensive and for the purpose of clinical use can be replaced by the cheaper prints obtained from RepRap printed copies. Conclusions Scanning of the traditional plaster casts to obtain a digital model offers a pragmatic approach. The scans can subsequently be used as a template to print the plaster casts as required. Using 3D printers can replace traditional plaster casts primarily due to their accuracy and price.
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Affiliation(s)
- Magdalena Kasparova
- Department of Stomatology, 2nd Medical Faculty, Charles University Prague, V Uvalu 84, 150 06, Prague 5, Czech Republic.
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Pei Y, Shi F, Chen H, Wei J, Zha H, Jiang R, Xu T. Personalized Tooth Shape Estimation From Radiograph and Cast. IEEE Trans Biomed Eng 2012; 59:2400-11. [PMID: 22084040 DOI: 10.1109/tbme.2011.2174993] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yuru Pei
- Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, Peking University, Beijing 100871, China.
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Gyehyun Kim, Jeongjin Lee, Jinwook Seo, Wooshik Lee, Yeong-Gil Shin, Bohyoung Kim. Automatic Teeth Axes Calculation for Well-Aligned Teeth Using Cost Profile Analysis Along Teeth Center Arch. IEEE Trans Biomed Eng 2012; 59:1145-54. [DOI: 10.1109/tbme.2012.2185825] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Wu T, Liao W, Dai N. Three-dimensional statistical model for gingival contour reconstruction. IEEE Trans Biomed Eng 2012; 59:1086-93. [PMID: 22249593 DOI: 10.1109/tbme.2012.2183368] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Optimal gingival contours around restored teeth and implants are of critical importance for restorative success and esthetics. This paper describes a novel computer-aided methodology for building a 3-D statistical model of gingival contours from a 3-D scan dental dataset and reconstructing missing gingival contours in partially edentulous patients. The gingival boundaries were first obtained from the 3-D dental model through a discrete curvature analysis and shortest path searching algorithm. Based on the gingival shape differential characteristics, the boundaries were demarcated to construct the gingival contour of each individual tooth. Through B-spline curve approximation to each gingival contour, the control points of the B-spline curves are used as the shape vector for training the model. Statistical analysis results demonstrate that the method can give a simple but compact model that effectively capture the most important variations in arch width and shape as well as gingival morphology and position. Within this statistical model, the morphologically plausible missing contours can be inferred based on a nonlinear optimization fitting from the global similarity transformation, the model shape deformation and a Mahalanobis prior. The reconstruction performance is evaluated through large simulated experimental data and a real patient case, which demonstrates the effectiveness of this approach.
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Affiliation(s)
- Ting Wu
- College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
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Kumar Y, Janardan R, Larson B, Moon J. Improved Segmentation of Teeth in Dental Models. ACTA ACUST UNITED AC 2011. [DOI: 10.3722/cadaps.2011.211-224] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Coleman SA, Scotney BW, Suganthan S. Edge detecting for range data using Laplacian operators. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2010; 19:2814-2824. [PMID: 20494852 DOI: 10.1109/tip.2010.2050733] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Feature extraction in image data has been investigated for many years, and more recently the problem of processing images containing irregularly distributed data has become prominent. Range data are now commonly used in the areas of image processing and computer vision. However, due to the data irregularity found in range images that occurs with a variety of image sensors, direct image processing, in particular edge detection, is a non-trivial problem. Typically, irregular range data would require to be interpolated to a regular grid prior to processing. One example of an edge detection technique than can be directly applied to range images is the scan-line approximation, but this does not employ exact data locations. Therefore, we present novel Laplacian operators that can be applied directly to irregularly distributed data, and in particular we focus on application to irregularly distributed 3D range data for the purpose of edge detection. Within the data distribution framework commonly occurring in range data acquisition devices, our results illustrate that the approach works well over a range of levels of irregularity of data distribution. The use of Laplacian operators on range data is also found to be much less susceptible to noise than the traditional use of Laplacian operators on intensity images.
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Chang YB, Xia JJ, Gateno J, Xiong Z, Zhou X, Wong STC. An automatic and robust algorithm of reestablishment of digital dental occlusion. IEEE TRANSACTIONS ON MEDICAL IMAGING 2010; 29:1652-1663. [PMID: 20529735 PMCID: PMC5668907 DOI: 10.1109/tmi.2010.2049526] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
In the field of craniomaxillofacial (CMF) surgery, surgical planning can be performed on composite 3-D models that are generated by merging a computerized tomography scan with digital dental models. Digital dental models can be generated by scanning the surfaces of plaster dental models or dental impressions with a high-resolution laser scanner. During the planning process, one of the essential steps is to reestablish the dental occlusion. Unfortunately, this task is time-consuming and often inaccurate. This paper presents a new approach to automatically and efficiently reestablish dental occlusion. It includes two steps. The first step is to initially position the models based on dental curves and a point matching technique. The second step is to reposition the models to the final desired occlusion based on iterative surface-based minimum distance mapping with collision constraints. With linearization of rotation matrix, the alignment is modeled by solving quadratic programming. The simulation was completed on 12 sets of digital dental models. Two sets of dental models were partially edentulous, and another two sets have first premolar extractions for orthodontic treatment. Two validation methods were applied to the articulated models. The results show that using our method, the dental models can be successfully articulated with a small degree of deviations from the occlusion achieved with the gold-standard method.
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Affiliation(s)
- Yu-Bing Chang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77841 USA
| | - James J. Xia
- Department of Oral and Maxillofacial Surgery, The Methodist Hospital Research Institute, and Department of Surgery (Oral and Maxillofacial Surgery), Weil Medical College of Cornell University, Houston, TX 77030 USA and also with Departments of Pediatric Surgery and Orthodontics, University of Texas Health Science Center, Houston, TX 77030 USA
| | - Jaime Gateno
- Department of Oral and Maxillofacial Surgery, the Methodist Hospital Research Institute, and Department of Surgery (Oral and Maxillofacial Surgery), Weil Medical College of Cornell University, Houston, TX 77030 USA
| | - Zixiang Xiong
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77841 USA
| | | | - Stephen T. C. Wong
- Center for Biotechnology and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weill Medical College of Cornell University, Houston, TX 77030 USA
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Single-Tooth Modeling for 3D Dental Model. Int J Biomed Imaging 2010; 2010. [PMID: 20689718 PMCID: PMC2906768 DOI: 10.1155/2010/535329] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2009] [Revised: 12/09/2009] [Accepted: 03/22/2010] [Indexed: 11/17/2022] Open
Abstract
An integrated single-tooth modeling scheme is proposed for the 3D dental model acquired by optical digitizers. The cores of the modeling scheme are fusion regions extraction, single tooth shape restoration, and single tooth separation. According to the “valley” shape-like characters of the fusion regions between two adjoining teeth, the regions of the 3D dental model are analyzed and classified based on the minimum curvatures of the surface. The single tooth shape is restored according to the bioinformation along the hole boundary, which is generated after the fusion region being removed. By using the extracted boundary from the blending regions between the teeth and soft tissues as reference, the teeth can be separated from the 3D dental model one by one correctly. Experimental results show that the proposed method can achieve satisfying modeling results with high-degree approximation of the real tooth and meet the requirements of clinical oral medicine.
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Xia JJ, Chang YB, Gateno J, Xiong Z, Zho X. Automated digital dental articulation. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2010; 13:278-86. [PMID: 20879410 PMCID: PMC5663470 DOI: 10.1007/978-3-642-15711-0_35] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
Articulating digital dental models is often inaccurate and very time-consuming. This paper presents an automated approach to efficiently articulate digital dental models to maximum intercuspation (MI). There are two steps in our method. The first step is to position the models to an initial position based on dental curves and a point matching algorithm. The second step is to finally position the models to the MI position based on our novel approach of using iterative surface-based minimum distance mapping with collision constraints. Finally, our method was validated using 12 sets of digital dental models. The results showed that using our method the digital dental models can be accurately and effectively articulated to MI position.
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Affiliation(s)
- James J Xia
- The Methodist Hospital Research Institute, Houston, Texas, USA.
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Kronfeld T, Brunner D, Brunnett G. Snake-Based Segmentation of Teeth from Virtual Dental Casts. ACTA ACUST UNITED AC 2010. [DOI: 10.3722/cadaps.2010.221-233] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Akhoondali H, Zoroofi R, Shirani G. Rapid Automatic Segmentation and Visualization of Teeth in CT-Scan Data. ACTA ACUST UNITED AC 2009. [DOI: 10.3923/jas.2009.2031.2044] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Chen H, Lowe AA, de Almeida FR, Wong M, Fleetham JA, Wang B. Three-dimensional computer-assisted study model analysis of long-term oral-appliance wear. Part 1: Methodology. Am J Orthod Dentofacial Orthop 2008; 134:393-407. [PMID: 18774086 DOI: 10.1016/j.ajodo.2006.10.030] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2005] [Revised: 10/01/2006] [Accepted: 10/01/2006] [Indexed: 10/21/2022]
Abstract
INTRODUCTION The aim of this study was to test a 3-dimensional (3D) computer-assisted dental model analysis system that uses selected landmarks to describe tooth movement during treatment with an oral appliance. METHODS Dental casts of 70 patients diagnosed with obstructive sleep apnea and treated with oral appliances for a mean time of 7 years 4 months were evaluated with a 3D digitizer (MicroScribe-3DX, Immersion, San Jose, Calif) compatible with the Rhinoceros modeling program (version 3.0 SR3c, Robert McNeel & Associates, Seattle, Wash). A total of 86 landmarks on each model were digitized, and 156 variables were calculated as either the linear distance between points or the distance from points to reference planes. Four study models for each patient (maxillary baseline, mandibular baseline, maxillary follow-up, and mandibular follow-up) were superimposed on 2 sets of reference points: 3 points on the palatal rugae for maxillary model superimposition, and 3 occlusal contact points for the same set of maxillary and mandibular model superimpositions. The patients were divided into 3 evaluation groups by 5 orthodontists based on the changes between baseline and follow-up study models. RESULTS Digital dental measurements could be analyzed, including arch width, arch length, curve of Spee, overbite, overjet, and the anteroposterior relationship between the maxillary and mandibular arches. A method error within 0.23 mm in 14 selected variables was found for the 3D system. The statistical differences in the 3 evaluation groups verified the division criteria determined by the orthodontists. CONCLUSIONS The system provides a method to record 3D measurements of study models that permits computer visualization of tooth position and movement from various perspectives.
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Affiliation(s)
- Hui Chen
- Department of Orthodontics, Faculty of Stomatology, Capital University of Medical Sciences, Beijing, China.
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Goularas D, Djemal K, Mannoussakis Y. 3D image modelling and specific treatments in orthodontics domain. Appl Bionics Biomech 2007. [DOI: 10.1080/11762320701754753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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Wu X, Gao H, Heo H, Chae O, Cho J, Lee S, Lee YK. Improved B-Spline Contour Fitting Using Genetic Algorithm for the Segmentation of Dental Computerized Tomography Image Sequences. J Imaging Sci Technol 2007. [DOI: 10.2352/j.imagingsci.technol.(2007)51:4(328)] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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