This review presents an in-depth study of the literature on segmentation methods applied in dental imaging. Ten segmentation methods were studied and categorized according to the type of the segmentation method (region-based, threshold-based, cluster-based, boundary-based or watershed-based), type of X-ray images used (intra-oral or extra-oral) and characteristics of the dataset used to evaluate the methods in the state-of-the-art works. We found that the literature has primarily focused on threshold-based segmentation methods (54%). 80% of the reviewed papers have used intra-oral X-ray images in their experiments, demonstrating preference to perform segmentation on images of already isolated parts of the teeth, rather than using extra-oral X-rays, which show tooth structure of the mouth and bones of the face. To fill a scientific gap in the field, a novel data set based on extra-oral X-ray images are proposed here. A statistical comparison of the results found with the 10 image segmentation methods over our proposed data set comprised of 1,500 images is also carried out, providing a more comprehensive source of performance assessment. Discussion on limitations of the methods conceived over the past year as well as future perspectives on exploiting learning-based segmentation methods to improve performance are also provided.
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。
CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images
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）
On Benchmarking Iris Recognition within a Head-mounted Display for AR/VR Application
Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形研究所（IGD））；TU Darmstadt（达姆施塔特工业大学）；The Norwegian Colour and Visual Computing Laboratory, NTNU（挪威色彩与视觉计算实验室，挪威科技大学（NTNU））；Norwegian Biometrics Laboratory, NTNU（挪威生物识别实验室，挪威科技大学（NTNU））；Mathematical and Applied Visual Computing（数学与应用视觉计算（TU Darmstadt））；Mathematical and Applied Visual Computing, TU Darmstadt（数学与应用视觉计算，达姆施塔特工业大学）
Anabranch Network for Camouflaged Object Segmentation
University of Dayton（代顿大学）；Institute of Industrial Science, the University of Tokyo（东京大学生产技术研究所）；The Graduate University for Advanced Studies (SOKENDAI)（综合研究大学院大学）；University of Science, VNU-HCM（越南国立大学胡志明市分校科学大学）；National Institute of Informatics（国立信息学研究所）
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Auckland Bioengineering Institute, University of Auckland（奥克兰大学生物工程研究所）；Beihang University（北京航空航天大学）；Peking University（北京大学）；SenseTime Inc（商汤科技）；Tencent Jarvis Laboratory（腾讯Jarvis实验室）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen University（深圳大学）；Wuhan University（武汉大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；EPITA Research and Development Laboratory（EPITA研发实验室）；Imperial College London（伦敦帝国学院）；Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Bologna（博洛尼亚大学）；Eindhoven University of Technology（埃因霍温理工大学）；Kings College London（伦敦国王学院）；University of Magdeburg（马格德堡大学）；Heidelberg University Hospital（海德堡大学医院）；Leiden University Medical Center（莱顿大学医学中心）；Universitat Pompeu Fabra（庞培法布拉大学）；Physense（Physense机构）
Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms
Nanjing Agricultural University（南京农业大学）；The University of Tokyo（东京大学）；Professor Jayashankar Telangana Agricultural State University（贾亚尚卡尔·特伦甘纳农业州立大学）；Kyoto University（京都大学）；Huazhong University of Science and Technology（华中科技大学）；Huazhong Agricultural University（华中农业大学）；Chinese Academy of Agricultural Sciences（中国农业科学院）；Chinese Academy of Science（中国科学院）；Shenyang Agricultural University（沈阳农业大学）；Jilin Academy of Agricultural Sciences（吉林省农业科学院）；Yuan Long Ping High-Tech Agriculture Co., Ltd.（袁隆平高科技农业有限公司）
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。
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation
Northwestern University（西北大学）；IIIT Delhi（德里印度信息技术学院）；NSIT Delhi（德里国家信息技术学院）；Space Research Institute (IWF) of Austrian Academy of Sciences（奥地利科学院空间研究所）
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）
Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University Hospital（海德堡大学医院）；University of Heidelberg（海德堡大学）；National Center for Tumor Diseases, Partner Site Dresden（国家肿瘤疾病中心德累斯顿分中心）；HIDSS4Health – Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；University of Calabria（卡拉布里亚大学）；understandAI GmbH（understandAI 有限公司）；University of Tübingen（蒂宾根大学）；BMW Group（宝马集团）；KARL STORZ SE & Co. KG（卡尔史托斯公司）；TU Dresden（德累斯顿工业大学）
Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation
School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机科学与工程学院）；Department of Data Science & AI, Faculty of Information Technology, Monash University（莫纳什大学信息技术学院数据科学与人工智能系）；Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究院）
Robust Medical Instrument Segmentation Challenge 2019
German Cancer Research Center（德国癌症研究中心）；University of Heidelberg（海德堡大学）；Heidelberg University Hospital（海德堡大学医院）；caresyntax；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；SimulaMet；Arctic University of Norway（挪威北极大学）；Oslo Metropolitan University（奥斯都会大学）；University of Electronic Science and Technology of China（电子科技大学）；National Center for Tumor Diseases（国家肿瘤疾病中心）；Technische Universität Dresden（德累斯顿工业大学）；Helmholtz-Zentrum Dresden - Rossendorf（德累斯顿-罗森多夫亥姆霍兹中心）；Universidad de los Andes（安第斯大学）；Peking University（北京大学）；Xiamen University（厦门大学）
