Skeletal bone age assessment (BAA), as an essential imaging examination, aims at evaluating the biological and structural maturation of human bones. In the clinical practice, Tanner and Whitehouse (TW2) method is a widely-used method for radiologists to perform BAA. The TW2 method splits the hands into Region Of Interests (ROI) and analyzes each of the anatomical ROI separately to estimate the bone age. Because of considering the analysis of local information, the TW2 method shows accurate results in practice. Following the spirit of TW2, we propose a novel model called Anatomical Local-Aware Network (ALA-Net) for automatic bone age assessment. In ALA-Net, anatomical local extraction module is introduced to learn the hand structure and extract local information. Moreover, we design an anatomical patch training strategy to provide extra regularization during the training process. Our model can detect the anatomical ROIs and estimate bone age jointly in an end-to-end manner. The experimental results show that our ALA-Net achieves a new state-of-the-art single model performance of 3.91 mean absolute error (MAE) on the public available RSNA dataset. Since the design of our model is well consistent with the well recognized TW2 method, it is interpretable and reliable for clinical usage.
Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment
University of California Los Angeles（加州大学洛杉矶分校）；Zhejiang University（浙江大学）；The First Affiliated Hospital, College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）
Relational Reasoning Network (RRN) for Anatomical Landmarking
University of Central Florida（中佛罗里达大学）；Ther-AI, LLC（Ther-AI有限责任公司）；National Institute of Dental and Craniofacial Research (NIDCR), National Institutes of Health (NIH)（美国国立卫生研究院下属国立牙科与颅面研究所）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；Northwestern University（西北大学）；Sheikh Zyed Children’s Hospital（谢赫·扎耶德儿童医院）；George Washington University（乔治·华盛顿大学）
Multiple Myeloma Lesion Segmentation on Whole-Body Diffusion-Weighted Imaging via Efficient Anatomical Anticipation and Multimodal Confirmation
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统全国重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College（中国医学科学院北京协和医学院北京协和医院放射科）
BoneAgeTW2: Automated Skeletal Maturation Assessment via the Tanner-Whitehouse 2 Method, Deep Learning, and Clinical Report Generation with Distribution Curves
AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns
Faculty of Engineering, University of Peradeniya（工程学院，珀德尼亚大学）；Faculty of Dental Sciences, University of Peradeniya（牙科学院，珀德尼亚大学）；Simula Metropolitan Center for Digital Engineering（模拟 Metropolitan 数字工程中心）
An AI-driven Assessment of Bone Density as a Biomarker Leading to the Aging Law
Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Nuclear Medicine Department, Peking Union Medical College Hospital（北京大学医学部核医学科）；Department of Nuclear Medicine, First Affiliated Hospital of Sun Yat-sen University（中山大学第一附属医院核医学科）
Evaluating the Clinical Impact of Generative Inpainting on Bone Age Estimation
Universidade Federal de São Paulo（巴西圣保罗联邦大学）；Universidade de São Paulo（巴西圣保罗大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Dasa；Bunkerhill Health（Bunkerhill健康）
Self-accumulative Vision Transformer for Bone Age Assessment Using the Sauvegrain Method
VUNO Inc.（VUNO公司）；University of Ulsan, College of Medicine（蔚山大学医学院）；Asan Medical Center（峨山医学中心）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Clinical evaluation of semi-automatic opensource algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action
Medical University of Graz（格拉茨医科大学）；Graz University of Technology（格拉茨工业大学）；Shanghai Jiao Tong University（上海交通大学）
A New Angle on Bones: Robust Pose Estimation in X-Ray and Ultrasound
Medical Informatics, University of Lübeck（吕贝克大学医学信息学系）；Institut of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院放射学与核医学研究所）；Paediatric Surgery, University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院小儿外科）；EchoScout GmbH
Attend-and-Refine: Interactive keypoint estimation and quantitative cervical vertebrae analysis for bone age assessment
Kim Jaechul Graduate School of Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea（韩国科学技术院人工智能研究生院）；Korea University Anam Hospital, Seoul 02708, Republic of Korea（韩国大学安安医院）；Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea（釜山大学医学院_asan医院）
Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment
The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong University（香港大学）；The Hong Kong University of Science and Technology Shenzhen Research Institute（香港科技大学深圳研究院）
BAPGAN: GAN-based Bone Age Progression of Femur and Phalange X-ray Images
LPIXEL Inc.（LPIXEL公司）；Saitama Prefectural University（埼玉县立大学）；Okayama City General Medical Center, Okayama City Hospital（冈山市综合医疗中心 冈山市立医院）；Okayama University（冈山大学）
Bonnet: Ultra-fast whole-body bone segmentation from CT scans
ELLIS Institute Finland（埃利斯研究所芬兰分校）；Aalto University（艾尔沃斯大学）；Clemson University（克莱姆森大学）；ETH Zurich（苏黎世联邦理工学院）；Fujian Medical University Union Hospital（福建省医科大学附属医院）；Fujian Medical University（福建省医科大学）
EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria（维也纳医科大学医学数据科学中心人工智能研究所）；Christian Doppler Lab for Artificial Intelligence in Retina, Medical University of Vienna, Austria（维也纳医科大学视网膜人工智能克里斯蒂安·多普勒实验室）
Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion
São Carlos Institute of Physics, University of São Paulo（圣保罗大学圣卡洛斯物理研究所）；Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University（圣保罗州立大学生物科学、人文与精确科学研究所）；Federal University of Catalão（卡塔朗联邦大学）；Royal Museum for Central Africa（比利时皇家非洲博物馆）；Ghent University（根特大学）；KERMIT（KERMIT机构）
Transformer-Based Multi-Region Segmentation and Radiomic Analysis of HR-pQCT Imaging for Osteoporosis Classification
Weldon School of Biomedical Engineering, Purdue University（普渡大学生物医学工程学院）；Division of Bone and Mineral Diseases, Washington University Medicine（华盛顿大学医学院骨科与矿物质疾病部）；Department of Anatomy, Cell Biology & Physiology, Indiana University School of Medicine（印第安纳大学医学院解剖学、细胞生物学与生理学部）；Department of Physical Therapy, School of Health & Human Sciences, Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校物理治疗系）
Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Osaka University（大阪大学）；Keio University School of Medicine（庆应义塾大学医学部）；Hitachi Ltd.（日立制作所）；Ehime University（爱媛大学）
Opportunistic Bone-Loss Screening from Routine Knee Radiographs Using a Multi-Task Deep Learning Framework with Sensitivity-Constrained Threshold Optimization
ORBIT Lab, College of Medicine and Biological Information Engineering, Northeastern University（ORBIT实验室，医学院和生物信息工程学院，东北大学）；Department of Radiology, Liaoning Provincial Key Laboratory of Medical Imaging, Liaoning Provincial Key Laboratory of Imaging Technology and Artificial Intelligence, Shengjing Hospital of China Medical University（放射科，辽宁省医学影像重点实验室，辽宁省影像技术与人工智能重点实验室，中国医科大学盛京医院）；Rehabilitation Center, Liaoning Provincial Key Laboratory of Medical Imaging, Liaoning Provincial Key Laboratory of Imaging Technology and Artificial Intelligence, Shengjing Hospital of China Medical University（康复中心，辽宁省医学影像重点实验室，辽宁省影像技术与人工智能重点实验室，中国医科大学盛京医院）；The University of Sydney, Sydney Musculoskeletal Health and the Kolling Institute, Northern Clinical School, Faculty of Medicine and Health and the Northern Sydney Local Health District（悉尼大学，悉尼骨科健康与Kolling研究所，北方临床医学院，医学院与健康学院，北悉尼地方卫生区）
Advances in Kidney Biopsy Lesion Assessment through Dense Instance Segmentation
Leiden University（莱顿大学）；Leiden University Medical Center（莱顿大学医学中心）；Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学医学中心（阿姆斯特丹大学））；University Medical Center Utrecht（乌得勒支大学医学中心）；University Hospitals Leuven（鲁汶大学医院）；KU Leuven（鲁汶大学）；Van’t Hoff Institute for Molecular Sciences, University of Amsterdam（阿姆斯特丹大学范特霍夫分子科学研究所）
Anatomically Faithful Artifact Suppression in SENSE Accelerated Brain MRI
East China Normal University（华东师范大学）；Affiliated Hospital of Guizhou Medical University（贵州医科大学附属医院）；West China Hospital, Sichuan University（四川大学华西医院）；Chinese Academy of Medical Sciences（中国医学科学院）；RadioDynamic Medical（镭动医疗）；Lucile Packard Children’s Hospital（露西尔·帕卡德儿童医院）
Sequential Spatial-Temporal Network for Interpretable Automatic Ultrasonic Assessment of Fetal Head during labor
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Sydney School of Health Sciences, The University of Sydney（悉尼大学悉尼健康科学学院）；School of Science, Edith Cowan University（伊迪丝·考恩大学理学院）；Orange Hospital（奥兰治医院）
Deep Learning-Based Cross-Anatomy CT Synthesis Using Adapted nnResU-Net with Anatomical Feature Prioritized Loss
Univ. Bordeaux, CNRS, LaBRI, UMR 5800, F-33400 Talence, France（波尔多大学，法国国家科学研究中心，LaBRI研究院，UMR 5800）；Universidad Autónoma de Madrid, Madrid, 28049, Spain（马德里自治大学，西班牙）；Pazmany Peter Catholic University, Budapest, 1083, Hungary（佩斯大学，匈牙利）；RIKEN Center for Integrative Medical Sciences, Medical Data Deep Learning Team, Tokyo, Japan（日本再生医学科学中心，医学数据深度学习团队）
Artificial Intelligence for the Assessment of Peritoneal Carcinosis during Diagnostic Laparoscopy for Advanced Ovarian Cancer
IRCAD, Research Institute against Digestive Cancer, Strasbourg, France（IRCAD消化道癌症研究机构，法国斯特拉斯堡）；University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France（斯特拉斯堡大学，法国）；Bioimage Analysis Center, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy（生物图像分析中心，意大利罗马）
Mammographic Breast Positioning Assessment via Deep Learning
Istanbul Technical University（伊斯坦布尔理工大学）；Acibadem Healthcare Group（阿西巴德姆医疗集团）；Acibadem Mehmet Ali Aydinlar University（阿西巴德姆穆罕默德·阿里·艾丁拉尔大学）；Sisli Hamidiye Etfal Training and Research Hospital（锡什利哈米迪耶埃特法尔培训与研究医院）
