We proposed a convolutional neural network for vertex classification on 3-dimensional dental meshes, and used it to detect teeth margins. An expanding layer was constructed to collect statistic values of neighbor vertex features and compute new features for each vertex with convolutional neural networks. An end-to-end neural network was proposed to take vertex features, including coordinates, curvatures and distance, as input and output each vertex classification label. Several network structures with different parameters of expanding layers and a base line network without expanding layers were designed and trained by 1156 dental meshes. The accuracy, recall and precision were validated on 145 dental meshes to rate the best network structures, which were finally tested on another 144 dental meshes. All networks with our expanding layers performed better than baseline, and the best one achieved an accuracy of 0.877 both on validation dataset and test dataset.
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…
Segmenting dental plaque from images of medical reagent staining provides valuable information for diagnosis and the determination of follow-up treatment plan. However, accurate dental plaque segmentation is a challenging task that requires identifying teeth and dental plaque subjected to semantic-blur regions (i.e., confused boundaries in border regions between teeth and dental plaque) and complex variations of instance shapes, which are not fully addressed by existing methods. Therefore, we propose a semantic decomposition network (SDNet) that introduces two single-task branches to separatel…
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）
U-Mamba2: Scaling State Space Models for Dental Anatomy Segmentation in CBCT
Centre for Oral, Clinical & Translational Sciences, King’s College London, United Kingdom（口腔、临床与转化科学中心，伦敦国王学院，英国）；Surrey Institute for People-Centred AI, University of Surrey, United Kingdom（以人为本的人工智能研究院，萨里大学，英国）
Seoul National University（首尔国立大学）；Soong-sil University（崇实大学）
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 regularizati…
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）
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 …
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…
TeethDreamer: 3D Teeth Reconstruction from Five Intra-oral Photographs
The University of Hong Kong（香港大学）；ShanghaiTech University（上海科技大学）；Shanghai Ninth People’s Hospital, School of Medicine, Shanghai JiaoTong University（上海交通大学医学院附属第九人民医院）；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）
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）
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 …
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 …
Analysis of dental radiographs is an important part of the diagnostic process in daily clinical practice. Interpretation by an expert includes teeth detection and numbering. In this project, a novel solution based on adaptive histogram equalization and convolution neural network (CNN) is proposed, which automatically performs the task for dental x-rays. In order to improve the detection accuracy, we propose three pre-processing techniques to supplement the baseline CNN based on some prior domain knowledge. Firstly, image sharpening and median filtering are used to remove impulse noise, and the…
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Chung Shan Medical University（中山医学大学）
Clinical finding summaries from an orthopantomogram, or a dental panoramic radiograph, have significant potential to improve patient communication and speed up clinical judgments. While orthopantomogram is a first-line tool for dental examinations, no existing work has explored the summarization of findings from it. A finding summary has to find teeth in the imaging study and label the teeth with several types of past treatments. To tackle the problem, we developDeepOPG that breaks the summarization process into functional segmentation and tooth localization, the latter of which is further ref…
Geometric-Guided Few-Shot Dental Landmark Detection with Human-Centric Foundation Model
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China（电子与计算机工程系，香港科学与技术大学）；Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China（牙科学院儿童牙科与正畸学系，香港大学）
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…
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…
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）
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）
ImplantMamba: Long-range Sequential Modeling Mamba For Dental Implant Position Prediction
School of Artifical Intelligence, Shenzhen University, Shenzhen, China（人工智能学院，深圳大学，中国深圳）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, China（大数据系统计算技术国家工程实验室，深圳大学，中国）；Huangpu People's Hospital, Zhongshan City, China（中山市黄埔人民医院，中国）；Department of Stomatology, Shenzhen University General Hospital, Shenzhen, China（口腔科，深圳大学附属医院，中国深圳）
STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation
Communication University of Zhejiang（浙江传媒学院）；West China Hospital of Stomatology, Sichuan University（四川大学华西口腔医院）；Lishui University（丽水学院）；Hangzhou Geriatric Stomatology Hospital（杭州老年口腔医院）；Hangzhou Dental Hospital Group（杭州市口腔医院集团）；Hangzhou Dianzi University（杭州电子科技大学）；Suzhou Research Institute of Shandong University（山东大学苏州研究院）；Shanghai Jiao Tong University（上海交通大学）；Shenzhen University（深圳大学）；Queen Mary University of London（伦敦大学玛丽皇后学院）；Shandong University（山东大学）；Sichuan Provincial People’s Hospital（四川省人民医院）；University of Electronic Science and Technology of China（电子科技大学）
本文介绍了 STS MICCAI 2023 挑战赛，旨在利用半监督算法解决 2D 全景 X 射线和 3D CBCT 牙齿分割问题，并总结了排名靠前队伍的方法，以推动牙科自动分析发展。
Gdańsk University of Technology（格但斯克工业大学）；Master Level Technologies（Master Level Technologies公司）；Dental Sense Medicover（Medicover牙科诊所）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）
A microcamera firmly attached to a dental handpiece allows dentists to continuously monitor the progress of conservative dental procedures. Video enhancement in video-assisted dental interventions alleviates low-light, noise, blur, and camera handshakes that collectively degrade visual comfort. To this end, we introduce a novel deep network for multi-task video enhancement that enables macro-visualization of dental scenes. In particular, the proposed network jointly leverages video restoration and temporal alignment in a multi-scale manner for effective video enhancement. Our experiments on vi…
Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge
Department of Oral；Maxillofacial Surgery, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, Netherlands；organization= State Key Lab of CAD\&CG, Zhejiang University, Hangzhou, 310058 , country= China；organization= Department of Computer Graphics；Multimedia, Brno University of Technology , city= Brno , country= Czech Republic；text= National Dental Centre Singapore；organization= Guangxi Colleges；Universities Key Laboratory of Intelligent Software ,country= Wuzhou University；text= National University of Singapore；organization= Department of Computer Science , country = University of Copenhagen；organization= School of Software, Shandong University , country= China；Centre de Recherche en Num\' e rique de Sfax, Laboratory of Signals, Systems, Artificial Intelligence；Inria, Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, France
Mesh based segmentation for automated margin line generation on incisors receiving crown treatment
King Fahd University of Petroleum and Minerals（法赫德国王石油与矿物大学）；Polytechnique 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 Dentaire 公司）
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Xi’an Jiaotong University（西安交通大学）；The University of Hong Kong（香港大学）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能有限公司）；Korea University（高丽大学）
The ability to segment teeth precisely from digitized 3D dental models is an essential task in computer-aided orthodontic surgical planning. To date, deep learning based methods have been popularly used to handle this task. State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation. This, however, has the drawback of ignoring the different geometric meanings provided by those raw attributes. This issue might possibly confuse the network in learning di…
University of Isfahan（伊斯法罕大学）；Shahrekord University（沙赫尔库尔德大学）
Up to now, researchers have applied segmentation techniques in their studies on teeth images, with construction on tooth root length and depth. In this paper, a new approach to the exact identification of the filled points of the tooth is proposed. In this method, the filled teeth are detection by applying the Bit-Plane algorithm on the OPG images. The novelty of the proposed method is that we can use it in medicine for the detection of dental filling and we calculate and present the area of the filled points which may help dentists to assess the filled point of the tooth. The experimental res…
Exploring the Role of Convolutional Neural Networks (CNN) in Dental Radiography Segmentation: A Comprehensive Systematic Literature Review
Research Groups in Intelligent Machines (REGIM Lab), University of Sfax, National Engineering School of Sfax (ENIS)（智能机器研究组（REGIM实验室），斯法克斯大学，斯法克斯国家工程学院）
Seoul National University（首尔国立大学）；Soongsil University（崇实大学）
Individual tooth segmentation and identification from cone-beam computed tomography images are preoperative prerequisites for orthodontic treatments. Instance segmentation methods using convolutional neural networks have demonstrated ground-breaking results on individual tooth segmentation tasks, and are used in various medical imaging applications. While point-based detection networks achieve superior results on dental images, it is still a challenging task to distinguish adjacent teeth because of their similar topologies and proximate nature. In this study, we propose a point-based tooth loc…
DE-KAN: A Kolmogorov Arnold Network with Dual Encoder for accurate 2D Teeth Segmentation
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；National Engineering Research Center of Oral Biomaterials and Digital Medical Devices（口腔生物材料与数字医疗设备国家工程研究中心）；Department of Statistics and Data Science, Jahangirnagar University（Jahangirnagar大学统计与数据科学系）；Faculty of Information Technology, Beijing University of Technology（北京理工大学信息学院）；Department of Orthodontics, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices（北京大学口腔医学院附属口腔医院、口腔疾病国家临床医学研究中心、口腔生物材料与数字医疗设备国家工程研究中心）
DE-KAN 通过双编码器 Kolmogorov Arnold 网络实现高精度 2D 牙齿分割，实验显示其在 Dice 系数上比现有方法提升达 4.7%。
FD-SOS: Vision-Language Open-Set Detectors for Bone Fenestration and Dehiscence Detection from Intraoral Images
The Hong Kong University of Science and Technology（香港科技大学）；The University of Hong Kong（香港大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳研究院）
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。
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。
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Stomatological Hospital of Chongqing Medical University（重庆医科大学附属口腔医院）；Macau University of Science and Technology（澳门科技大学）；Xi’an Jiaotong University（西安交通大学）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能科技有限公司）；The University of Hong Kong（香港大学）
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 dif…
