Radiologists and doctors make use of X-ray images of the non-dominant hands of children and infants to assess the possibility of genetic conditions and growth abnormalities. This is done by assessing the difference between the actual extent of growth found using the X-rays and the chronological age of the subject. The assessment was done conventionally using The Greulich Pyle (GP) or Tanner Whitehouse (TW) approach. These approaches require a high level of expertise and may often lead to observer bias. Hence, to automate the process of assessing the X-rays, and to increase its accuracy and efficiency, several machine learning models have been developed. These machine-learning models have several differences in their accuracy and efficiencies, leading to an unclear choice for the suitable model depending on their needs and available resources. Methods: In this study, we have analyzed the 3 most widely used models for the automation of bone age prediction, which are the Xception model, VGG model and CNN model. These models were trained on the preprocessed dataset and the accuracy was measured using the MAE in terms of months for each model. Using this, the comparison between the models was done. Results: The 3 models, Xception, VGG, and CNN models have been tested for accuracy and other relevant factors.
Concordia Institute for Information Systems Engineering（康考迪亚大学信息系统工程研究所）；Concordia University（康考迪亚大学）
Bone age is an important measure for assessing the skeletal and biological maturity of children. Delayed or increased bone age is a serious concern for pediatricians, and needs to be accurately assessed in a bid to determine whether bone maturity is occurring at a rate consistent with chronological age. In this paper, we introduce a unified deep learning framework for bone age assessment using instance segmentation and ridge regression. The proposed approach consists of two integrated stages. In the first stage, we employ an image annotation and segmentation model to annotate and segment the h…
Cornell University（康奈尔大学）；CuraCloud Corporation（库拉克劳德公司）；UNC Charlotte（北卡罗来纳大学夏洛特分校）；University at Albany, State University at New York（纽约州立大学奥尔巴尼分校）
Computerized automatic methods have been employed to boost the productivity as well as objectiveness of hand bone age assessment. These approaches make predictions according to the whole X-ray images, which include other objects that may introduce distractions. Instead, our framework is inspired by the clinical workflow (Tanner-Whitehouse) of hand bone age assessment, which focuses on the key components of the hand. The proposed framework is composed of two components: a Mask R-CNN subnet of pixelwise hand segmentation and a residual attention network for hand bone age assessment. The Mask R-C…
Manipal Institute of Technology, MAHE（玛尼帕尔高等教育学院马尼帕尔理工学院）
Bone age assessment (BAA) is a standard method for determining the age difference between skeletal and chronological age. Manual processes are complicated and necessitate the expertise of experts. This is where deep learning comes into play. In this study, pre-trained models like VGG-16, InceptionV3, XceptionNet, and MobileNet are used to assess the bone age of the input data, and their mean average errors are compared and evaluated to see which model predicts the best.
Lyft Inc.（Lyft公司）；Neuromation OU（Neuromation公司）；University of Michigan（密歇根大学）；Massachusetts Institute of Technology（麻省理工学院）
Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017. The dataset for this competition is consisted of 12.6k radiological images of left hand labeled by the bone age and sex of patients. Our approach utilizes several deep learning architectures: U-Net, ResNet-50, and custom VGG-style neural networks trained end-to-end. We use images of whole hands as…
The bone age, reflecting the degree of development of the bones, can be used to predict the adult height and detect endocrine diseases of children. Both examinations of radiologists and variability of operators have a significant impact on bone age assessment. To decrease human intervention , machine learning algorithms are used to assess the bone age automatically. However, conventional supervised deep-learning methods need pre-labeled data. In this paper, based on the convolutional auto-encoder with constraints (CCAE), an unsupervised deep-learning model proposed in the classification of the…
Bone age assessment gives us evidence to analyze the children growth status and the rejuvenation involved chronological and biological ages. All the previous works consider left-hand X-ray image of a child in their works. In this paper, we carry out a study on estimating human age using whole-body bone CT images and a novel convolutional neural network. Our model with additional connections shows an effective way to generate a massive number of vital features while reducing overfitting influence on small training data in the medical image analysis research area. A dataset and a comparison with…
Imsight Medical Technology, Co., Ltd.（深圳视见医疗科技有限公司）；Shenzhen University（深圳大学）；The Chinese University of Hong Kong（香港中文大学）
Bone age is one of the most important indicators for assessing bone's maturity, which can help to interpret human's growth development level and potential progress. In the clinical practice, bone age assessment (BAA) of X-ray images requires the joint consideration of the appearance and location information of hand bones. These kinds of information can be effectively captured by the relation of different anatomical parts of hand bone. Recently developed methods differ mostly in how they model the part relation and choose useful parts for BAA. However, these methods neglect the mining of relati…
BoneAgeTW2: Automated Skeletal Maturation Assessment via the Tanner-Whitehouse 2 Method, Deep Learning, and Clinical Report Generation with Distribution Curves
Peking University（北京大学）；Yizhun Medical AI Co., Ltd（一准医疗人工智能有限公司）
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 …
Bone age assessment is an important clinical trial to measure skeletal child maturity and diagnose of growth disorders. Conventional approaches such as the Tanner-Whitehouse (TW) and Greulich and Pyle (GP) may not perform well due to their large inter-observer and intra-observer variations. In this paper, we propose a finger joint localization strategy to filter out most non-informative parts of images. When combining with the conventional full image-based deep network, we observe a much-improved performance. % Our approach utilizes full hand and specific joints images for skeletal maturity pr…
Universidad de los Andes（安第斯大学）；Fundación Santa Fe de Bogotá（圣菲波哥大基金会）
Bone Age Assessment (BAA) is a task performed by radiologists to diagnose abnormal growth in a child. In manual approaches, radiologists take into account different identity markers when calculating bone age, i.e., chronological age and gender. However, the current automated Bone Age Assessment methods do not completely exploit the information present in the patient's metadata. With this lack of available methods as motivation, we present SIMBA: Specific Identity Markers for Bone Age Assessment. SIMBA is a novel approach for the task of BAA based on the use of identity markers. For this purpos…
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）
Bone age assessment (BAA) is clinically important as it can be used to diagnose endocrine and metabolic disorders during child development. Existing deep learning based methods for classifying bone age use the global image as input, or exploit local information by annotating extra bounding boxes or key points. However, training with the global image underutilizes discriminative local information, while providing extra annotations is expensive and subjective. In this paper, we propose an attention-guided approach to automatically localize the discriminative regions for BAA without any extra ann…
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）
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）
This study presents a novel approach to bone age assessment (BAA) using a multi-view, multi-task classification model based on the Sauvegrain method. A straightforward solution to automating the Sauvegrain method, which assesses a maturity score for each landmark in the elbow and predicts the bone age, is to train classifiers independently to score each region of interest (RoI), but this approach limits the accessible information to local morphologies and increases computational costs. As a result, this work proposes a self-accumulative vision transformer (SAT) that mitigates anisotropic behav…
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）
Convolutional Neural Networks play a key role in bone age assessment for investigating endocrinology, genetic, and growth disorders under various modalities and body regions. However, no researcher has tackled bone age progression/regression despite its valuable potential applications: bone-related disease diagnosis, clinical knowledge acquisition, and museum education. Therefore, we propose Bone Age Progression Generative Adversarial Network (BAPGAN) to progress/regress both femur/phalange X-ray images while preserving identity and realism. We exhaustively confirm the BAPGAN's clinical potent…
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）
Department of Biomedical Engineering, University of Basel（巴塞尔大学生物医学工程系）；Department of Biomedicine, Musculoskeletal Research, University of Basel（巴塞尔大学生物医学系肌肉骨骼研究部）；University of Basel（巴塞尔大学）
Segmentation of distinct bones plays a crucial role in diagnosis, planning, navigation, and the assessment of bone metastasis. It supplies semantic knowledge to visualisation tools for the planning of surgical interventions and the education of health professionals. Fully supervised segmentation of 3D data using Deep Learning methods has been extensively studied for many tasks but is usually restricted to distinguishing only a handful of classes. With 125 distinct bones, our case includes many more labels than typical 3D segmentation tasks. For this reason, the direct adaptation of most establ…
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）
Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However - due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice. In this retrospective, randomized, co…
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）
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）
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）
Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play an important role in computer-aided disease assessment. Most deep regression methods train the neural network with a single regression loss function like MSE or L1 loss. In this paper, we propose the first contrastive learning framework for deep image regression, namely AdaCon, which consists of a feature learning branch via a novel adaptive-margin contrastive loss and a regression prediction branch. Our method incorporates label distance…
This study applies a technique to expand the number of images to a level that allows deep learning. And the applicability of the Sauvegrain method through deep learning with relatively few elbow X-rays is studied. The study was composed of processes similar to the physicians' bone age assessment procedures. The selected reference images were learned without being included in the evaluation data, and at the same time, the data was extended to accommodate the number of cases. In addition, we adjusted the X-ray images to better images using U-Net and selected the ROI with RPN + so as to be able t…
Singapore-ETH Center（新加坡-ETH中心）；ETH Zuerich（苏黎世联邦理工学院）；University of Melbourne（墨尔本大学）
Recently developed automatic dense image matching algorithms are now being implemented for DSM/DTM production, with their pixel-level surface generation capability offering the prospect of partially alleviating the need for manual and semi-automatic stereoscopic measurements. In this paper, five commercial/public software packages for 3D surface generation are evaluated, using 5cm GSD imagery recorded from a UAV. Generated surface models are assessed against point clouds generated from mobile LiDAR and manual stereoscopic measurements. The software packages considered are APS, MICMAC, SURE, Pi…
In remote sensing, images acquired by various earth observation satellites tend to have either a high spatial and low spectral resolution or vice versa. Pansharpening is a technique which aims to improve spatial resolution of multispectral image. The challenges involve in the pansharpening are not only to improve the spatial resolution but also to preserve spectral quality of the multispectral image. In this paper, various pansharpening algorithms are discussed and classified based on approaches they have adopted. Using MATLAB image processing toolbox, several state-of-art pan-sharpening algor…
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）
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Google Inc.（谷歌公司）
Many objective video quality assessment (VQA) algorithms include a key step of temporal pooling of frame-level quality scores. However, less attention has been paid to studying the relative efficiencies of different pooling methods on no-reference (blind) VQA. Here we conduct a large-scale comparative evaluation to assess the capabilities and limitations of multiple temporal pooling strategies on blind VQA of user-generated videos. The study yields insights and general guidance regarding the application and selection of temporal pooling models. In addition, we also propose an ensemble pooling …
Comparative Assessment of Multimodal Earth Observation Data for Soil Moisture Estimation
BEYOND EO Centre, IAASARS, National Observatory of Athens（BEYOND EO中心、IAASARS、雅典国家天文台）；Artificial Intelligence, Wageningen University & Research（人工智能，瓦赫宁根大学与研究所）
The process of quantifying image quality consists of engineering the quality features and pooling these features to obtain a value or a map. There has been a significant research interest in designing the quality features but pooling is usually overlooked compared to feature design. In this work, we compare the state of the art quality and content-based spatial pooling strategies and show that although features are the key in any image quality assessment, pooling also matters. We also propose a quality-based spatial pooling strategy that is based on linearly weighted percentile pooling (WPP). …
University College London（伦敦大学学院）；Royal Free & University College Medical School（皇家自由与大学学院医学院）；Royal Free London Foundation Trust（皇家自由伦敦基金会信托）
Breast cancer is the most prevalent cancer type in women, and while its survival rate is generally high the aesthetic outcome is an increasingly important factor when evaluating different treatment alternatives. 3D scanning and reconstruction techniques offer a flexible tool for building detailed and accurate 3D breast models that can be used both pre-operatively for surgical planning and post-operatively for aesthetic evaluation. This paper aims at comparing the accuracy of low-cost 3D scanning technologies with the significantly more expensive state-of-the-art 3D commercial scanners in the c…
Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime
PT Mitra Solusi Telematika；Ingram School of Engineering（英格拉姆工程学院）；Texas State University（德克萨斯州立大学）；Department of Civil Engineering（土木工程系）；Rowan University（罗文大学）；Department of Geography and Environmental Studies（地理与环境研究系）
We investigate a model for image/video quality assessment based on building a set of codevectors representing in a sense some basic properties of images, similar to well-known CORNIA model. We analyze the codebook building method and propose some modifications for it. Also the algorithm is investigated from the point of inference time reduction. Both natural and synthetic images are used for building codebooks and some analysis of synthetic images used for codebooks is provided. It is demonstrated the results on quality assessment may be improves with the use if synthetic images for codebook c…
IEEE Computational Intelligence Society（IEEE计算智能学会）；Institute of Electrical and Electronics Engineers（电气和电子工程师协会）；Blekinge Institute of Technology（布莱金厄理工学院）
In the mobile communication field, some of the video applications boosted the interest of robust methods for video quality assessment. Out of all existing methods, We Preferred, No Reference Video Quality Assessment is the one which is most needed in situations where the handiness of reference video is partially available. Our research interest lies in formulating and melding effective features into one model based on human visualizing characteristics. Our work explores comparative study between Supervised and unsupervised learning methods. Therefore, we implemented support vector regression a…
Unsupervised Machine Learning for Osteoporosis Diagnosis Using Singh Index Clustering on Hip Radiographs
Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology（计算机技术系，计算学院，SRM科学与技术学院）；Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia（通信技术与网络系，计算机科学与信息技术学院，马来西亚国立大学）；Department of Orthopaedics, Sri Ramachandra Medical College（骨科系，Sri Ramachandra医学院）
A comparative assessment of global building and settlement datasets across geographic and settlement contexts
The World Bank, Global Facility for Disaster Reduction and Recovery（世界银行，全球减灾与恢复基金）；Center for Geospatial Analytics, Clark University（克拉克大学地理空间分析中心）；University of Twente（特文特大学）；School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）；School of Architecture and Planning, Morgan State University（摩根州立大学建筑与规划学院）
Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare
City University of Hong Kong（香港城市大学）；Shanghai Jiao Tong University（上海交通大学）；Jiangxi University of Finance and Economics（江西财经大学）；Nanyang Technological University（南洋理工大学）
Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-Supervision, Mixture of Experts and Multi-Task Integration
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；DAMO Academy, Alibaba Group（阿里云达摩院）；Union Hospital, Huazhong University of Science and Technology（华中科技大学同济医院）；Xiangya Hospital, Central South University（中南大学湘雅医院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Hupan Lab, Hangzhou 310023, China（沪幻实验室）
