Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth reformation planning and implant guide simulations. However, the presence of severe metal artifacts in CBCT images hinders the accurate segmentation of each individual tooth. In this study, we propose a neural network for pixel-wise labeling to exploit an instance segmentation framework that is robust to metal artifacts. Our method comprises of three steps: 1) image cropping and realignment by pose regressions, 2) metal-robust individual tooth detection, and 3) segmentation. We first extract the alignment information of the patient by pose regression neural networks to attain a volume-of-interest (VOI) region and realign the input image, which reduces the inter-overlapping area between tooth bounding boxes. Then, individual tooth regions are localized within a VOI realigned image using a convolutional detector. We improved the accuracy of the detector by employing non-maximum suppression and multiclass classification metrics in the region proposal network. Finally, we apply a convolutional neural network (CNN) to perform individual tooth segmentation by converting the pixel-wise labeling task to a distance regression task. Metal-intensive image augmentation is also employed for a robust segmentation of metal artifacts. The result shows that our proposed method outperforms other state-of-the-art methods, especially for teeth with metal artifacts. The primary significance of the proposed method is two-fold: 1) an introduction of pose-aware VOI realignment followed by a robust tooth detection and 2) a metal-robust CNN framework for accurate tooth segmentation.
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…
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。
Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis
School of Mechanical, Electrical & Information Engineering, Shandong University（山东大学机电与信息工程学院）；SDU-ANU Joint Science College, Shandong University（山东大学山东大学-澳大利亚国立大学联合科学学院）；Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院生物医药与健康工程研究院）；School of Software Engineering, Electronic and Information Engineering, Xi'an Jiaotong University（西安交通大学软件与电子信息工程学院）；Department of Radiology, Shenzhen Children's Hospital（深圳市儿童医院放射科）
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…
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…
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 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 …
Differentiable Collision-Supervised Tooth Arrangement Network with a Decoupling Perspective
School of Software, Tsinghua University（清华大学软件学院）；Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital, Dental（清华大学附属北京清华长庚医院口腔科）；Peking University Hospital of Stomatology（北京大学口腔医院）
ToothSegNet: Image Degradation meets Tooth Segmentation in CBCT Images
Zhejiang University（浙江大学）；Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University（浙江大学浙江大学-伊利诺伊大学厄巴纳-香槟分校学院）；Angelalign Inc.（时代天使公司）；Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine（浙江大学医学院口腔医学院口腔医院）
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）
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）
Shape-preserving Tooth Segmentation from CBCT Images Using Deep Learning with Semantic and Shape Awareness
organization= Department of Control Science；Engineering, School of Electronics；Information Engineering, Tongji University , city= Shanghai , postcode= 201804 , country= China；organization= Shanghai Institute of Intelligent Science；Technology, Tongji University , city= Shanghai , postcode= 200092 , country= China；organization= School of Biomedical Engineering \& State Key Laboratory of Advanced Medical Materials；Devices, ShanghaiTech University , city= Shanghai , postcode= 201210 , country= China；organization= Stomatological Hospital of Henan Province, The First Affiliated Hospital of Zhengzhou University, School；Hospital of Stomatology of Zhengzhou University , city= Zhenzhou , postcode= 450052 , country= China；organization= Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University , city= Hangzhou , postcode= 310016 , country= China；Implant Dentistry, Faculty of Dentistry, The University of Hong Kong , city= Hong Kong SAR , postcode= 999077 , country= China；organization= School of Computer Science；Engineering, Southeast University , city= Nanjing , postcode= 211102 , country= China；organization= Department of Oral Implantology, School；Hospital of Stomatology, Shanghai Engineering Research Center of Tooth Restoration；Regeneration, Tongji University , city= Shanghai , postcode= 200070 , country= China
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）
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。
Sichuan University（四川大学）；Zhejiang University School of Medicine（浙江大学医学院）；Harvard University（哈佛大学）；Zhejiang University（浙江大学）；AngelAlign Tech Inc.（时代天使科技有限公司）；Shanghai Jiao Tong University（上海交通大学）
A critical step in virtual dental treatment planning is to accurately delineate all tooth-bone structures from CBCT with high fidelity and accurate anatomical information. Previous studies have established several methods for CBCT segmentation using deep learning. However, the inherent resolution discrepancy of CBCT and the loss of occlusal and dentition information largely limited its clinical applicability. Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental m…
Anatomy Aware Cascade Network: Bridging Epistemic Uncertainty and Geometric Manifold for 3D Tooth Segmentation
School of Information Engineering, Nanchang University（信息工程学院，南昌大学）；First Affiliated Hospital, Jiangxi Medical College, Nanchang University（江西医学院第一附属医院，南昌大学）；Second Clinical Medical College, Nanchang University（南昌大学第二临床医学院）；First Clinical Medical College, Nanchang University（南昌大学第一临床医学院）
Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth Segmentation
Department of Computer Science and Engineering（计算机科学与工程系）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry（牙科学院儿童牙科与正畸科）；The University of Hong Kong（香港大学）；Delun Dental Hospital（德尔伦牙科医院）；Department of Chemical and Biological Engineering（化学与生物工程系）；Division of Life Science（生命科学系）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）；State Key Laboratory of Nervous System Disorders（神经系统紊乱国家重点实验室）
U-Mamba2-SSL for Semi-Supervised Tooth and Pulp 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所，萨里大学，英国）
GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation
Sano Centre for Computational Medicine（桑诺计算医学中心）；Jagiellonian University（雅盖隆大学）；Jagiellonian University Medical College（雅盖隆大学医学院）；Warsaw University of Technology（华沙技术大学）；Research Institute IDEAS（IDEAS研究所）；Massachusetts General Hospital, Harvard Medical School（麻省总医院，哈佛医学院）
National Institute for Mathematical Sciences（国家数学科学研究所）；Yonsei University（延世大学）
This paper describes the mathematical structure of the ill-posed nonlinear inverse problem of low-dose dental cone-beam computed tomography (CBCT) and explains the advantages of a deep learning-based approach to the reconstruction of computed tomography images over conventional regularization methods. This paper explains the underlying reasons why dental CBCT is more ill-posed than standard computed tomography. Despite this severe ill-posedness, the demand for dental CBCT systems is rapidly growing because of their cost competitiveness and low radiation dose. We then describe the limitations o…
Seoul National University（首尔大学）；Harvard University（哈佛大学）；Seoul National University School of Dentistry（首尔大学牙医学院）；Soong-sil University（崇实大学）
Computerized registration between maxillofacial cone-beam computed tomography (CT) images and a scanned dental model is an essential prerequisite in surgical planning for dental implants or orthognathic surgery. We propose a novel method that performs fully automatic registration between a cone-beam CT image and an optically scanned model. To build a robust and automatic initial registration method, our method applies deep-pose regression neural networks in a reduced domain (i.e., 2-dimensional image). Subsequently, fine registration is performed via optimal clusters. Majority voting system ac…
Yonsei University（延世大学）；National Institute for Mathematical Sciences（国家数学科学研究所）
Low-dose dental cone beam computed tomography (CBCT) has been increasingly used for maxillofacial modeling. However, the presence of metallic inserts, such as implants, crowns, and dental filling, causes severe streaking and shading artifacts in a CBCT image and loss of the morphological structures of the teeth, which consequently prevents accurate segmentation of bones. A two-stage metal artifact reduction method is proposed for accurate 3D low-dose maxillofacial CBCT modeling, where a key idea is to utilize explicit tooth shape prior information from intra-oral scan data whose acquisition do…
Eindhoven University of Technology（埃因霍温理工大学）；Philips IGT（飞利浦图像引导治疗部门）
Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based on deep learning models for X-ray images often use custom architectures that employ extensive CAD models and simulated data for training purposes. Recent RGB-based methods opt to solve pose estimation problems using small datasets, making them more attractive for the X-ray domain where medical data is scarcely available. We refine an existing RGB-based model (SingleShotPose) to estimate the 6D pose of a marked cube fr…
MICCAI STS 2024 Challenge: Semi-Supervised Instance-Level Tooth Segmentation in Panoramic X-ray and CBCT Images
Innovation Center for Electronic Design Automation Technology（电子设计自动化技术创新中心）；School of Cyberspace（网络空间学院）；Hangzhou Dianzi University（杭州电子科技大学）；Hangzhou Geriatric Stomatology Hospital（杭州老年牙科医院）；Shenzhen University（深圳大学）；Queen Mary University of London（伦敦大学Queen Mary分校）；Shandong University（山东大学）；Xidian University（西安电子科技大学）；Shanghai Jiao Tong University（上海交通大学）；Harbin Institute of Technology（哈尔滨工业大学）；The University of Hong Kong（香港大学）；Chinese Academy of Sciences（中国科学院）；Yunnan Provincial Stomatology Hospital（云南省牙科医院）；Shanghai MediWorks Precision Instruments Co., Ltd（上海 MediWorks 精密仪器有限公司）；University of Leicester（莱斯特大学）
A Globally Convergent Algorithm for Total Scaled-Gradient Variation via Cone-Constrained Bilinear Decomposition
Yau Mathematical Sciences Center, Tsinghua University（清华大学丘成桐数学科学中心）；School of Mathematical Sciences, Nankai University（南开大学数学科学学院）；Institute of Mathematics and Interdisciplinary Sciences, Tianjin Normal University（天津师范大学数学与交叉科学研究院）
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））
Conebeam CT using a circular trajectory is quite often used for various applications due to its relative simple geometry. For conebeam geometry, Feldkamp, Davis and Kress algorithm is regarded as the standard reconstruction method, but this algorithm suffers from so-called conebeam artifacts as the cone angle increases. Various model-based iterative reconstruction methods have been developed to reduce the cone-beam artifacts, but these algorithms usually require multiple applications of computational expensive forward and backprojections. In this paper, we develop a novel deep learning approac…
Warsaw University of Technology（华沙理工大学）；Poznan University of Medical Sciences（波兹南医科大学）；Medical Centre Leeuwarden（吕伐登医疗中心）；Tooploox
Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis. This review presents the application of convolutional neural networks in one of the fields of dentistry - orthodontics. Advances in medical imaging technologies and methods allow CNNs to be used in orthodontics to shorten the planning time of orthodontic treatment, including an automatic search of landmarks on cephalometric X-ray images, tooth segmentation on Cone-Beam Computed Tomography (CBCT) images or digital models, and…
Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Optical diffraction tomography (ODT) is an emerging 3D imaging technique that is used for the 3D reconstruction of the refractive index (RI) for semi-transparent samples. Various inverse models have been proposed to reconstruct the 3D RI based on the holographic detection of different samples such as the Born and the Rytov approximations. However, such approximations usually suffer from the so-called missing-cone problem that results in an elongation of the final reconstruction along the optical axis. Different iterative schemes have been proposed to solve the missing cone problem relying on p…
