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. The first stage is pre-processing. The second and main part of this algorithm calculated rotation degree and uses the integral projection method for tooth isolation. Experimental results show that this algorithm is robust and achieves high accuracy.
School of Biomedical Informatics, The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心生物医学信息学院）；Department of Periodontics and Dental Hygiene, The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心牙周病与口腔卫生学系）；Department of Applied Data Science, San Jose State University（圣何塞州立大学应用数据科学系）
Periodontitis is a biofilm-related chronic inflammatory disease characterized by gingivitis and bone loss in the teeth area. Approximately 61 million adults over 30 suffer from periodontitis (42.2%), with 7.8% having severe periodontitis in the United States. The measurement of radiographic bone loss (RBL) is necessary to make a correct periodontal diagnosis, especially if the comprehensive and longitudinal periodontal mapping is unavailable. However, doctors can interpret X-rays differently depending on their experience and knowledge. Computerized diagnosis support for doctors sheds light on …
PRAD: Periapical Radiograph Analysis Dataset and Benchmark Model Development
College of Computer Science, Nankai University（南开大学计算机学院）；Key Laboratory of Data and Intelligent System Security, Ministry of Education, China（数据与智能系统安全重点实验室（中国教育部））；Department of stomatology, Tianjin Union Medical Center, The First Affiliated Hospital of Nankai University（南开大学第一附属医院天津 Union 医疗中心口腔科）；College of Mechanical Engineering, Taiyuan University of Technology（太原理工大学机械工程学院）；Haihe Lab of ITAI（海河人工智能与信息技术实验室）
Sorbonne Universités UPMC Paris 06（索邦大学 巴黎第六大学（皮埃尔和玛丽·居里大学））；CNRS（法国国家科学研究中心）
In this paper, we present a method to automatically compare multiple radiographs in order to find the identity of a patient out of the dental features. The method is based on the matching of image features, previously extracted by computer vision algorithms for image descriptor recognition. The principal application (being also our motivation to study the problem) of such a method would be in victim identification in mass disasters.
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…
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…
PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolution
Department of Dentistry（牙科系）；Oral Health, Aarhus University（口腔健康，奥胡斯大学）；Aarhus Institute of Advanced Studies, Aarhus University（奥胡斯大学高级研究 institute）；Department of Mathematics, Aarhus University（数学系，奥胡斯大学）；Faculty of Information Technology（信息科技学院）；Communication Sciences, Tampere University（传播科学学院，塔尔库大学）
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）
In order to study the application of artificial intelligence (AI) to dental imaging, we applied AI technology to classify a set of panoramic radiographs using (a) a convolutional neural network (CNN) which is a form of an artificial neural network (ANN), (b) representative image cognition algorithms that implement scale-invariant feature transform (SIFT), and (c) histogram of oriented gradients (HOG).
K-U-KAN: Koopman-Enhanced U-KAN for 3D Dental Reconstruction from a Single Panoramic X-ray Radiograph
Artificial Intelligence \& Image Processing Lab., Department of Information \& Communication Engineering, Sun Moon University, Asan-Si, South Korea；Department of Physics；Engineering Physics, Tulane University, New Orleans, LA, USA
Panoramic Dental Radiography (PDR) image processing is one of the most extensively used manual methods for gender determination in forensic medicine. With the assistance of the PDR images, a person's biological gender determination can be performed through analyzing skeletal structures expressing sexual dimorphism. Manual approaches require a wide range of mandibular parameter measurements in metric units. Besides being time-consuming, these methods also necessitate the employment of experienced professionals. In this context, deep learning models are widely utilized in the auto-analysis of ra…
ViT-NeBLa: A Hybrid Vision Transformer and Neural Beer-Lambert Framework for Single-View 3D Reconstruction of Oral Anatomy from Panoramic Radiographs
Artificial Intelligence and Image Processing Lab., Department of Information and Communication Engineering, Sun Moon University, South Korea（人工智能与图像处理实验室，信息与通信工程系，顺 Moon 大学，韩国）；Department of Physics and Engineering Physics, Tulane University, New Orleans, LA 70118, USA（物理系与工程物理系，路易斯安那大学，新奥尔良，路易斯安那州 70118，美国）
Silhouette-to-Contour Registration: Aligning Intraoral Scan Models with Cephalometric Radiographs
School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, China（人工智能与先进计算学院，西安交通大学利物浦大学，中国）；School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, United Kingdom（电子工程、电子科学与计算机科学学院，利物浦大学，英国）；School of Advanced Technology, Xi'an Jiaotong-Liverpool University, China（先进技术学院，西安交通大学利物浦大学，中国）；School of Physical Sciences, University of Liverpool, Liverpool, United Kingdom（物理科学学院，利物浦大学，英国）；School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, China（数学与物理学院，西安交通大学利物浦大学，中国）；Institute of Artificial Intelligence Innovation, Zhejiang University of Technology, China（人工智能创新研究院，浙江工业大学，中国）
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…
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays
Hasso Plattner Institute, University of Potsdam（波茨坦大学哈索·普拉特纳研究所）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Contraste Radiologia Odontológica（康特拉斯特牙科放射诊所）；Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院西奈山哈索·普拉特纳数字健康研究所）
An Explainable Machine Learning Approach for Age and Gender Estimation in Living Individuals Using Dental Biometrics
University of Essex（埃塞克斯大学）；Tel Aviv University（特拉维夫大学）；Oral Medicine and Oral Surgery（口腔医学与口腔外科）；Universitas Airlangga（艾尔朗加大学）；Boston University（波士顿大学）；King George’s Medical University（乔治国王医科大学）
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）
Segmentation of Mental Foramen in Orthopantomographs: A Deep Learning Approach
School of Computer Science and Electronic Engineering（计算机科学与电子工程学院）；University of Essex（埃塞克斯大学）；Faculty of Dental Medicine, University of Airlangga（艾尔朗加大学牙医学院）；Tel Aviv University（特拉维夫大学）；King George’s Medical University（乔治国王医科大学）
Towards Generalist Intelligence in Dentistry: Vision Foundation Models for Oral and Maxillofacial Radiology
Shanghai Jiao Tong University（上海交通大学）；Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Department of Oral Craniomaxillofacial（上海第九人民医院，上海交通大学医学院，口腔颅面医学部）；Shanghai Jiao Tong University, School of Computer Science（上海交通大学，计算机科学学院）；University of Electronic Science and Technology of China, School of Mechanical and Electrical Engineering（电子科技大学，机械与电子工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University, College of Stomatology（上海交通大学，口腔医学院）；National Center for Stomatology（国家口腔医学中心）；National Clinical Medical Research Center for Oral Diseases（国家口腔疾病临床医学研究中心）；Shanghai Key Laboratory of Stomatology（上海口腔医学重点实验室）；Shanghai Research Institute of Stomatology（上海口腔研究所）；Chinese Academy of Medical Science, Research Unit of Oral and Maxillofacial Regenerative Medicine（中国医学科学院，口腔颌面再生医学研究单元）
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…
3D teeth reconstruction from X-ray is important for dental diagnosis and many clinical operations. However, no existing work has explored the reconstruction of teeth for a whole cavity from a single panoramic radiograph. Different from single object reconstruction from photos, this task has the unique challenge of constructing multiple objects at high resolutions. To conquer this task, we develop a novel ConvNet X2Teeth that decomposes the task into teeth localization and single-shape estimation. We also introduce a patch-based training strategy, such that X2Teeth can be end-to-end trained for…
Radious: Unveiling the Enigma of Dental Radiology with BEIT Adaptor and Mask2Former in Semantic Segmentation
Gilan University of Medical Sciences（吉兰医科大学）；University of Tehran（德黑兰大学）；Hamedan University of Technology（哈马丹工业大学）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）
DentalX: Context-Aware Dental Disease Detection with Radiographs
Centre for Oral, Clinical \& Translational Sciences, King’s College London, United Kingdom Surrey Institute for People-Centred AI, University of Surrey, United Kingdom
Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning
Department of Dentistry and Oral Health（牙科与口腔健康系）；Aarhus University（奥胡斯大学）；Department of Electrical- and Computer Engineering（电气与计算机工程系）；Faculty of Information Technology and Communications Sciences（信息科技与通讯科学学院）；Tampere University（塔尔库大学）
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…
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）
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）
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）
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）
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）
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%。
Weighted Knowledge Distillation for Semi-Supervised Segmentation of Maxillary Sinus in Panoramic X-ray Images
Division of Electronics and Information Engineering, College of Engineering, Jeonbuk National University（全罗国立大学电子与信息工程系）；Manufacturing AI Research Center, Korea Institute of Industrial Technology (KITECH)（韩国工业技术研究院制造人工智能研究中心）；KITECH School, University of Science and Technology, Cheonan（科技大学校KITECH学院）；The Graduate School of Advanced Imaging Science, Multimedia & Film, Chung-Ang University（Chung-Ang大学高级影像科学研究生院，多媒体与电影）；Department of Oral and Maxillofacial Radiology and Wonkwang Dental Research Institute, College of Dentistry, Wonkwang University（Wonkwang大学口腔颌面放射科及Wonkwang牙科研究所）
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 Leicester（莱斯特大学）；University of Electronic Science and Technology of China（电子科技大学）；Hangzhou Dianzi University（杭州电子科技大学）
3D tooth segmentation is a prerequisite for computer-aided dental diagnosis and treatment. However, segmenting all tooth regions manually is subjective and time-consuming. Recently, deep learning-based segmentation methods produce convincing results and reduce manual annotation efforts, but it requires a large quantity of ground truth for training. To our knowledge, there are few tooth data available for the 3D segmentation study. In this paper, we establish a fully annotated cone beam computed tomography dataset CTooth with tooth gold standard. This dataset contains 22 volumes (7363 slices) w…
UTN- Universidad Tecnológica Nacional（阿根廷国家科技大学）；Famaf- Facultad de Matemática Astronomía y Física, Universidad Nacional de Córdoba（科尔多瓦国立大学数学天文物理学院）；Facultad de Ingeniería, FCEFyN, Universidad Nacional de Córdoba（科尔多瓦国立大学工程学院与自然科学与精确科学学院）；Universidad Nacional del Sur（南方国立大学）
In this paper, we study statistical classification accuracy of two different Markov field environments for pixelwise image segmentation, considering the labels of the image as hidden states and solving the estimation of such labels as a solution of the MAP equation. The emission distribution is assumed the same in all models, and the difference lays in the Markovian prior hypothesis made over the labeling random field. The a priori labeling knowledge will be modeled with a) a second order anisotropic Markov Mesh and b) a classical isotropic Potts model. Under such models, we will consider thre…
