Dental panoramic X-ray imaging is a popular diagnostic method owing to its very small dose of radiation. For an automated computer-aided diagnosis system in dental clinics, automatic detection and identification of individual teeth from panoramic X-ray images are critical prerequisites. In this study, we propose a point-wise tooth localization neural network by introducing a spatial distance regularization loss. The proposed network initially performs center point regression for all the anatomical teeth (i.e., 32 points), which automatically identifies each tooth. A novel distance regularization penalty is employed on the 32 points by considering $L_2$ regularization loss of Laplacian on spatial distances. Subsequently, teeth boxes are individually localized using a cascaded neural network on a patch basis. A multitask offset training is employed on the final output to improve the localization accuracy. Our method successfully localizes not only the existing teeth but also missing teeth; consequently, highly accurate detection and identification are achieved. The experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches by increasing the average precision of teeth detection by 15.71% compared to the best performing method. The accuracy of identification achieved a precision of 0.997 and recall value of 0.972. Moreover, the proposed network does not require any additional identification algorithm owing to the preceding regression of the fixed 32 points regardless of the existence of the teeth.
CUHK-Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Automatic tooth instance segmentation on 3D dental models is a fundamental task for computer-aided orthodontic treatments. Existing learning-based methods rely heavily on expensive point-wise annotations. To alleviate this problem, we are the first to explore a low-cost annotation way for 3D tooth instance segmentation, i.e., labeling all tooth centroids and only a few teeth for each dental model. Regarding the challenge when only weak annotation is provided, we present a dental arch prior-assisted 3D tooth segmentation method, namely DArch. Our DArch consists of two stages, including tooth ce…
3D tooth segmentation is an important task for digital orthodontics. Several Deep Learning methods have been proposed for automatic tooth segmentation from 3D dental models or intraoral scans. These methods require annotated 3D intraoral scans. Manually annotating 3D intraoral scans is a laborious task. One approach is to devise self-supervision methods to reduce the manual labeling effort. Compared to other types of point cloud data like scene point cloud or shape point cloud data, 3D tooth point cloud data has a very regular structure and a strong shape prior. We look at how much representat…
Cone-beam computed tomography (CBCT) is a valuable imaging method in dental diagnostics that provides information not available in traditional 2D imaging. However, interpretation of CBCT images is a time-consuming process that requires a physician to work with complicated software. In this work we propose an automated pipeline composed of several deep convolutional neural networks and algorithmic heuristics. Our task is two-fold: a) find locations of each present tooth inside a 3D image volume, and b) detect several common tooth conditions in each tooth. The proposed system achieves 96.3\% acc…
The University of Hong Kong（香港大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；The University of Edinburgh（爱丁堡大学）
In orthodontic treatment, a full tooth model consisting of both the crown and root is indispensable in making the treatment plan. However, acquiring tooth root information to obtain the full tooth model from CBCT images is sometimes restricted due to the massive radiation of CBCT scanning. Thus, reconstructing the full tooth shape from the ready-to-use input, e.g., the partial intra-oral scan and the 2D panoramic image, is an applicable and valuable solution. In this paper, we propose a neural network, called ToothInpaintor, that takes as input a partial 3D dental model and a 2D panoramic imag…
The computer-assisted radiologic informative report is currently emerging in dental practice to facilitate dental care and reduce time consumption in manual panoramic radiographic interpretation. However, the amount of dental radiographs for training is very limited, particularly from the point of view of deep learning. This study aims to utilize recent self-supervised learning methods like SimMIM and UM-MAE to increase the model efficiency and understanding of the limited number of dental radiographs. We use the Swin Transformer for teeth numbering, detection of dental restorations, and insta…
Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-beam computerized tomography (CBCT) images is a challenging problem because of the difficulty in separating an individual tooth from adjacent teeth and its surrounding alveolar bone. Thus, this paper proposes a fully automated method of identifying and segmenting 3D individual teeth from dental CBCT images. The proposed method addresses the aforementioned difficulty by developing a deep learning-based hierarchical multi-step model. First, it automatically generates upper and lower jaws panoramic images to …
TCATSeg: A Tooth Center-Wise Attention Network for 3D Dental Model Semantic Segmentation
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Beijing Stomatological Hospital, Capital Medical University（首都医科大学北京 stomatological Hospital）；LargeV Instrument Corporation, Ltd.（LargeV仪器有限公司）
Queen Mary University of London（伦敦玛丽女王大学）；Communication University of Zhejiang（浙江传媒学院）；University of Electronic Science and Technology of China（电子科技大学）；West China Hospital of Stomatology, Sichuan University（四川大学华西口腔医院）；University of Leicester（莱斯特大学）
Accurate tooth volume segmentation is a prerequisite for computer-aided dental analysis. Deep learning-based tooth segmentation methods have achieved satisfying performances but require a large quantity of tooth data with ground truth. The dental data publicly available is limited meaning the existing methods can not be reproduced, evaluated and applied in clinical practice. In this paper, we establish a 3D dental CBCT dataset CTooth+, with 22 fully annotated volumes and 146 unlabeled volumes. We further evaluate several state-of-the-art tooth volume segmentation strategies based on fully-supe…
Accurate 3D Prediction of Missing Teeth in Diverse Patterns for Precise Dental Implant Planning
School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；School of Mechanical, Electrical and Information Engineering, Shandong University（山东大学机电与信息工程学院）；School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学通信与信息工程学院）；Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University（上海交通大学医学院附属第九人民医院）；Hangzhou First People’s Hospital, Zhejiang University（浙江大学附属杭州市第一人民医院）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
ArchMap: Arch-Flattening and Knowledge-Guided Vision Language Model for Tooth Counting and Structured Dental Understanding
Xi'an Jiaotong-Liverpool University（西交利物浦大学）；University of Liverpool（利物浦大学）；Zhejiang University of Technology（浙江工业大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
A Multi-Stage Framework for 3D Individual Tooth Segmentation in Dental CBCT
School of Artificial Intelligence, Guilin University of Electronic Technology（桂林电子科技大学人工智能学院）；Guangxi Colleges and Universities Key Laboratory of AI Algorithm Engineering（广西高校人工智能算法工程重点实验室）
Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identification
Yonsei University（延世大学）；College of Dentistry, Yonsei University（延世大学牙科学院）；Oral Science Research Center（口腔科学研究中心）
We develop a Computer Aided Diagnosis (CAD) system, which enhances the performance of dentists in detecting wide range of dental caries. The CAD System achieves this by acting as a second opinion for the dentists with way higher sensitivity on the task of detecting cavities than the dentists themselves. We develop annotated dataset of more than 3000 bitewing radiographs and utilize it for developing a system for automated diagnosis of dental caries. Our system consists of a deep fully convolutional neural network (FCNN) consisting 100+ layers, which is trained to mark caries on bitewing radiog…
The University of Hong Kong（香港大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Texas A&M University（得克萨斯农工大学）
3D Morphable models of the human body capture variations among subjects and are useful in reconstruction and editing applications. Current dental models use an explicit mesh scene representation and model only the teeth, ignoring the gum. In this work, we present the first parametric 3D morphable dental model for both teeth and gum. Our model uses an implicit scene representation and is learned from rigidly aligned scans. It is based on a component-wise representation for each tooth and the gum, together with a learnable latent code for each of such components. It also learns a template shape …
Memory-Guided Point Cloud Completion for Dental Reconstruction
College of Information and Intelligent Science, Donghua University, Shanghai, China（信息与智能科学学院，东华大学，上海，中国）；Institute of Artificial Intelligence, Shanghai University, Shanghai, China（人工智能研究院，上海大学，上海，中国）
PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology
Faculty of Dentistry, The University of Hong Kong（香港大学牙医学院）；Imperial College London（帝国理工学院）；University of Science and Technology of China（中国科学技术大学）；Department of Data and Systems Engineering, The University of Hong Kong（香港大学数据与系统工程系）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）
Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training
School of Biomedical Engineering \& State Key Laboratory of Advanced Medical Materials；Devices, ShanghaiTech University, Shanghai, China Lingang Laboratory, Shanghai, China Shanghai United Imaging Intelligence Co. Ltd., Shanghai, China Shanghai Clinical Research；Trial Center, Shanghai, China Shanghai Linkedcare Information Technology Co., Ltd., Shanghai, China
National Institutes of Health（美国国立卫生研究院）；Purdue University（普渡大学）；Hill’s Pet Nutrition（希尔斯宠物营养公司）
Biofilm is a formation of microbial material on tooth substrata. Several methods to quantify dental biofilm coverage have recently been reported in the literature, but at best they provide a semi-automated approach to quantification with significant input from a human grader that comes with the graders bias of what are foreground, background, biofilm, and tooth. Additionally, human assessment indices limit the resolution of the quantification scale; most commercial scales use five levels of quantification for biofilm coverage (0%, 25%, 50%, 75%, and 100%). On the other hand, current state-of-t…
Federal University of Bahia（巴伊亚联邦大学）；Federal Institute of Bahia（巴伊亚联邦学院）；Southwest State University of Bahia（巴伊亚西南州立大学）
Deep learning has remarkably advanced in the last few years, supported by large labeled data sets. These data sets are precious yet scarce because of the time-consuming labeling procedures, discouraging researchers from producing them. This scarcity is especially true in dentistry, where deep learning applications are still in an embryonic stage. Motivated by this background, we address in this study the construction of a public data set of dental panoramic radiographs. Our objects of interest are the teeth, which are segmented and numbered, as they are the primary targets for dentists when sc…
Boundary feature fusion network for tooth image segmentation
College of Information Engineering, China Jiliang University（中国计量大学信息工程学院）；College of Optoelectronics, China Jiliang University（中国计量大学光电学院）；College of Computer and Control Engineering, Qiqihar University（齐齐哈尔大学计算机与控制工程学院）
针对牙齿与相邻组织边界模糊问题，本文提出融合边界特征提取与特征交叉融合模块的牙齿分割网络，在STS Data Challenge中获得0.91分，显著优于现有方法。
OralGPT-Plus: Learning to Use Visual Tools via Reinforcement Learning for Panoramic X-ray Analysis
The Hong Kong University of Science and Technology (GZ)（香港科学与技术大学）；Faculty of Dentistry, The University of Hong Kong（香港大学牙医学院）；School of Computer Science, Peking University（北京大学计算机学院）；Shanghai Jiao Tong University（上海交通大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）
Automatic teeth segmentation in panoramic x-ray images is an important research subject of the image analysis in dentistry. In this study, we propose a post-processing stage to obtain a segmentation map in which the objects in the image are separated, and apply this technique to tooth instance segmentation with U-Net network. The post-processing consists of grayscale morphological and filtering operations, which are applied to the sigmoid output of the network before binarization. A dice overlap score of 95.4 - 0.3% is obtained in overall teeth segmentation. The proposed post-processing stages…
Dental caries is one of the most chronic diseases involving the majority of the population during their lifetime. Caries lesions are typically diagnosed by radiologists relying only on their visual inspection to detect via dental x-rays. In many cases, dental caries is hard to identify using x-rays and can be misinterpreted as shadows due to different reasons such as low image quality. Hence, developing a decision support system for caries detection has been a topic of interest in recent years. Here, we propose an automatic diagnosis system to detect dental caries in Panoramic images for the f…
École Polytechnique de Montréal（蒙特利尔理工学院）；Centre d'intelligence artificielle appliquée (JACOBB)（应用人工智能中心（JACOBB））；Intellident Dentaire Inc.（Intellident牙科公司）
Teeth segmentation is an important topic in dental restorations that is essential for crown generation, diagnosis, and treatment planning. In the dental field, the variability of input data is high and there are no publicly available 3D dental arch datasets. Although there has been improvement in the field provided by recent deep learning architectures on 3D data, there still exists some problems such as properly identifying missing teeth in an arch. We propose to use spectral clustering as a self-supervisory signal to joint-train neural networks for segmentation of 3D arches. Our approach is …
Semi-supervised classification of dental conditions in panoramic radiographs using large language model and instance segmentation: A real-world dataset evaluation
Institute of Computing, Federal University of Bahia（巴伊亚联邦大学计算学院）；Federal Institute of Bahia（巴伊亚联邦学院）；Federal University of Maranhão（马拉尼昂联邦大学）；Harvard School of Public Health（哈佛大学公共卫生学院）；Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial（机械与工业工程科学与创新研究所）；Faculdade de Engenharia, Universidade do Porto（波尔图大学工程学院）；Dentistry Faculty, Federal University of Bahia（巴伊亚联邦大学牙科学院）
Innovative Tooth Segmentation Using Hierarchical Features and Bidirectional Sequence Modeling
School of Computer Science and Technology, Zhejiang Gongshang University（浙江工商大学计算机科学与技术学院）；School of Computer and Cyber Sciences, Communication University of China（中国传媒大学计算机与 cyber 科学学院）；Center of Big Data, China Digital Culture Group Co., Ltd（中国数字文化集团大数据中心）；Department of Stomatology, Tongxiang Hospital of Traditional Chinese Medicine（同祥中医医院口腔科）；School of Mathematical and Computational Sciences, Massey University（马希大学数学与计算科学学院）；Shining3D Tech Co., Ltd.（Shining3D科技有限公司）；School of Art Design, Zhejiang Gongshang University（浙江工商大学艺术设计学院）；Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology（浙江大数据与未来电子商务技术重点实验室）
Caries DETR: Tooth Structure-aware Prior and Lesion-aware Dynamic Loss Refinement for DETR Based Caries Detection
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；Department of Stomatology, Shenzhen University General Hospital（深圳大学附属医院口腔科）
National Institutes of Health（美国国立卫生研究院）；Purdue University（普渡大学）；Hill’s Pet Nutrition（希尔斯宠物营养公司）
Dental biofilm is the deposition of microbial material over a tooth substratum. Several methods have recently been reported in the literature for biofilm quantification; however, at best they provide a barely automated solution requiring significant input needed from the human expert. On the contrary, state-of-the-art automatic biofilm methods fail to make their way into clinical practice because of the lack of effective mechanism to incorporate human input to handle praxis or misclassified regions. Manual delineation, the current gold standard, is time consuming and subject to expert bias. In…
Designing a synthetic crown is a time-consuming, inconsistent, and labor-intensive process. In this work, we present a fully automatic method that not only learns human design dental crowns, but also improves the consistency, functionality, and esthetic of the crowns. Following success in point cloud completion using the transformer-based network, we tackle the problem of the crown generation as a point-cloud completion around a prepared tooth. To this end, we use a geometry-aware transformer to generate dental crowns. Our main contribution is to add a margin line information to the network, a…
Zhejiang University（浙江大学）；Angelalign Research Institute（时代天使研究院）；Angel Align Inc.（时代天使公司）；A*STAR Centre for Frontier AI Research (CFAR)（新加坡科技研究局前沿人工智能研究中心）；Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine（浙江大学医学院附属口腔医院）；Harvard University（哈佛大学）
Optical Intra-oral Scanners (IOS) are widely used in digital dentistry, providing 3-Dimensional (3D) and high-resolution geometrical information of dental crowns and the gingiva. Accurate 3D tooth segmentation, which aims to precisely delineate the tooth and gingiva instances in IOS, plays a critical role in a variety of dental applications. However, segmentation performance of previous methods are error-prone in complicated tooth-tooth or tooth-gingiva boundaries, and usually exhibit unsatisfactory results across various patients, yet the clinically applicability is not verified with large-sc…
DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray
Department of Quantitative Biomedicine, University of Zurich（苏黎世大学定量生物医学系）；Harvard Medical School（哈佛医学院）；Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；ETH AI Center, ETH Zurich（苏黎世联邦理工学院人工智能中心）；Center for Dental Medicine, University of Zurich（苏黎世大学牙科医学中心）；Department of Computer Engineering, Bogazici University（博雅大学计算机工程系）；Department of Endodontics, Istanbul Medipol University（伊斯坦布尔Medipol大学牙体医学系）；University of Oklahoma, College of Dentistry, Graduate Periodontics Department（俄克拉荷马大学牙科学院研究生牙周病学系）
FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image
Hangzhou Dianzi University（杭州电子科技大学）；College of Media Engineering, Communication University of Zhejiang（浙江传媒学院媒体工程学院）；Shandong University（山东大学）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
针对牙齿 CBCT 中伪影复杂、边界模糊的问题，本文提出 FDNet，用 LF-Wavelet 增强全局语义并结合 SAM 编码器细化边界，在基准上取得最优分割性能。
Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with Structured Output (SLSO) Framework
Gifu University（岐阜大学）；EyeTech Co., Ltd（EyeTech有限公司）；Asahi University（朝日大学）；Shiga University（滋贺大学）；The University of Osaka Dental Hospital（大阪大学齿科医院）
Artificial Intelligence to Assess Dental Findings from Panoramic Radiographs -- A Multinational Study
National Taiwan University（国立台湾大学）；National Taiwan University of Science and Technology（国立台湾科技大学）；Radboud University Medical Center（拉德堡德大学医学中心）；The Ohio State University（俄亥俄州立大学）；National Taiwan University Hospital（国立台湾大学附属医院）
Image processing techniques has been widely used in dental researches such as human identification and forensic dentistry, teeth numbering, dental carries detection and periodontal disease analysis. One of the most challenging parts in dental imaging is teeth segmentation and how to separate them from each other. In this paper, an automated method for teeth segmentation of Periapical dental x-ray images which contain at least one root-canalled tooth is proposed. The result of this approach can be used as an initial step in bone lesion detection. The proposed algorithm is made of two stages. Th…
Sparse Anatomical Prompt Semi-Supervised Learning with Masked Image Modeling for CBCT Tooth Segmentation
Institute of Innovative Research, Tokyo Institute of Technology（东京工业大学创新研究院）；Stomatological Hospital of Chongqing Medical University（重庆医科大学附属口腔医院）；School of Mechanical Engineering, Zhejiang University（浙江大学机械工程学院）
Advanced Deep Learning Techniques for Classifying Dental Conditions Using Panoramic X-Ray Images
Informatics, Oakland University, Rochester, Michigan, USA（信息学，奥克兰大学）；Department of Computer Science, Bowling Green State University, Ohio, USA（计算机科学系，布恩维尔州立大学）
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Panoramic X-ray (PX) provides a 2D picture of the patient's mouth in a panoramic view to help dentists observe the invisible disease inside the gum. However, it provides limited 2D information compared with cone-beam computed tomography (CBCT), another dental imaging method that generates a 3D picture of the oral cavity but with more radiation dose and a higher price. Consequently, it is of great interest to reconstruct the 3D structure from a 2D X-ray image, which can greatly explore the application of X-ray imaging in dental surgeries. In this paper, we propose a framework, named Oral-3D, to…
TeethGenerator: A two-stage framework for paired pre- and post-orthodontic 3D dental data generation
Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Beijing Stomatological Hospital, Capital Medical University（首都医科大学北京 stomatological Hospital）
OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors
Department of Computer Science and Engineering（计算机科学与工程系）；School of IT and Engineering（信息与工程学院）；Indian Institute of Technology Kharagpur（印度理工学院Kharagpur分校）；Kazakh-British Technical University（哈萨克-英国技术大学）；Intelligent Devices Institute（智能设备研究所）；St. Petersburg Electrotechnical University "LETI"（圣彼得堡电力大学"LETI"）；Department of Automation and Control Processes（自动化与控制过程系）
