Bone mineral density (BMD) is a clinically critical indicator of osteoporosis, usually measured by dual-energy X-ray absorptiometry (DEXA). Due to the limited accessibility of DEXA machines and examinations, osteoporosis is often under-diagnosed and under-treated, leading to increased fragility fracture risks. Thus it is highly desirable to obtain BMDs with alternative cost-effective and more accessible medical imaging examinations such as X-ray plain films. In this work, we formulate the BMD estimation from plain hip X-ray images as a regression problem. Specifically, we propose a new semi-supervised self-training algorithm to train the BMD regression model using images coupled with DEXA measured BMDs and unlabeled images with pseudo BMDs. Pseudo BMDs are generated and refined iteratively for unlabeled images during self-training. We also present a novel adaptive triplet loss to improve the model's regression accuracy. On an in-house dataset of 1,090 images (819 unique patients), our BMD estimation method achieves a high Pearson correlation coefficient of 0.8805 to ground-truth BMDs. It offers good feasibility to use the more accessible and cheaper X-ray imaging for opportunistic osteoporosis screening.
University of Maryland（马里兰大学）；PAII Inc.（PAII公司）；Alibaba Group DAMO Academy（阿里巴巴集团达摩院）；Xpeng Motors（小鹏汽车）；Ping An Technology（平安科技）；Chang Gung Memorial Hospital（长庚纪念医院）
Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g. via Dual-energy X-ray Absorptiometry (DXA). This paper proposes a method to predict BMD from Chest X-ray (CXR), one of the most commonly accessible and low-cost medical imaging examinations. Our method first automatically detects Regions of Interest (ROIs) of local CXR bone structures. Then a multi-ROI deep model with transformer encoder is developed to exploit both local and global information in the chest X-ray image for ac…
Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；CentraleSupélec（巴黎中央理工-高等电力学院）；Université Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；Osaka University Graduate School of Medicine（大阪大学医学研究生院）；Ehime University Graduate School of Medicine（爱媛大学医学研究生院）
A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip Arthroplasty
University of Science and Technology of China（中国科学技术大学）；The First Affiliated Hospital of the University of Science and Technology of China（中国科学技术大学附属第一医院）
The University of Iceland（冰岛大学）；ETH Zurich（苏黎世联邦理工学院）；The University of Calgary（卡尔加里大学）；McCaig Institute for Bone and Joint Health（麦凯格骨关节健康研究所）；The Icelandic Heart Association（冰岛心脏协会）；The Johns Hopkins University（约翰斯·霍普金斯大学）
Osteoporosis is a common bone disease that increases the risk of bone fracture. Hip-fracture risk screening methods based on finite element analysis depend on segmented computed tomography (CT) images; however, current femur segmentation methods require manual delineations of large data sets. Here we propose a deep neural network for fully automated, accurate, and fast segmentation of the proximal femur from CT. Evaluation on a set of 1147 proximal femurs with ground truth segmentations demonstrates that our method is apt for hip-fracture risk screening, bringing us one step closer to a clinic…
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）
University Hospital of Saint-Etienne（圣艾蒂安大学医院）；Hospices Civils de Lyon（里昂民用医院）；CREATIS（CREATIS研究所）；INSA-Lyon（里昂国立应用科学学院）；University of Strasbourg（斯特拉斯堡大学）
By highlighting the regions of the input image that contribute the most to the decision, saliency maps have become a popular method to make neural networks interpretable. In medical imaging, they are particularly well-suited to explain neural networks in the context of abnormality localization. However, from our experiments, they are less suited to classification problems where the features that allow to distinguish between the different classes are spatially correlated, scattered and definitely non-trivial. In this paper we thus propose a new paradigm for better interpretability. To this end …
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）
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）；Santa Clara University（圣克拉拉大学）
Identifying the design of a failed implant is a key step in preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time consuming and prone to error. Failure to identify the implant design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of total hip replacement (THR) implants from …
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Osaka University Graduate School of Medicine（大阪大学大学院医学系研究科）；Ehime University Graduate School of Medicine（爱媛大学大学院医学系研究科）
We propose a method for estimating the bone mineral density (BMD) from a plain x-ray image. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) provide high accuracy in diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. Measuring BMD from an x-ray image provides an opportunistic screening, which is potentially useful for early diagnosis. The previous methods that directly learn the relationship between x-ray images and BMD require a large training dataset to achieve high accuracy because of large intensity variations i…
The University of Iceland（冰岛大学）；ETH Zürich（苏黎世联邦理工学院）；The Icelandic Heart Association（冰岛心脏协会）；The Johns Hopkins University（约翰斯·霍普金斯大学）
Osteoporosis is a common bone disease that occurs when the creation of new bone does not keep up with the loss of old bone, resulting in increased fracture risk. Adults over the age of 50 are especially at risk and see their quality of life diminished because of limited mobility, which can lead to isolation and depression. We are developing a robust screening method capable of identifying individuals predisposed to hip fracture to address this clinical challenge. The method uses finite element analysis and relies on segmented computed tomography (CT) images of the hip. Presently, the segmentat…
University of Maryland College Park（马里兰大学学院公园分校）；PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Ping An Technology（平安科技）
Osteoporosis is a common chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, Dual-energy X-ray Absorptiometry (DXA). In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible, and low-cost medical image examinations. Our method first automatically detects Regions of Interest (ROIs) of local and global bone structures from the CXR. Then a multi-ROI model is developed to exploit both local and global information in the chest X-ray image for accurate …
We present a method to generate subject-specific cartilage for the hip joint. Given bone geometry, our approach is agnostic to image modality, creates conforming interfaces, and is well suited for finite element analysis. We demonstrate our method on ten hip joints showing anatomical shape consistency and well-behaved stress patterns. Our method is fast and may assist in large-scale biomechanical population studies of the hip joint when manual segmentation or training data is not feasible.
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…
Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray Image
Nara Institute of Science and Technology（奈良科学技术研究所）；Osaka University Graduate School of Medicine（大阪大学医学院）；Ehime University Graduate School of Medicine（爱媛大学医学院）；CentraleSupélec（中央高等电力学院）；Université Paris-Saclay（巴黎-萨克雷大学）
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
National University of the South（南方国立大学）；National Scientific and Technical Research Council（国家科学技术研究委员会）；University of Jaén（哈恩大学）；University of Valladolid（巴利亚多利德大学）；Universitätsinstitut für Radiologie, Neuroradiologie und Nuklearmedizin（大学放射学、神经放射学与核医学研究所）
We propose a 3D neural network with specific loss functions for quantitative computed tomography (QCT) noise reduction to compute micro-structural parameters such as tissue mineral density (TMD) and bone volume ratio (BV/TV) with significantly higher accuracy than using no or standard noise reduction filters. The vertebra-phantom study contained high resolution peripheral and clinical CT scans with simulated in vivo CT noise and nine repetitions of three different tube currents (100, 250 and 360 mAs). Five-fold cross validation was performed on 20466 purely spongy pairs of noisy and ground-tru…
Kiel University（基尔大学）；Oregon Health & Science University（俄勒冈健康与科学大学）
Osteoporosis is a common disease that increases fracture risk. Hip fractures, especially in elderly people, lead to increased morbidity, decreased quality of life and increased mortality. Being a silent disease before fracture, osteoporosis often remains undiagnosed and untreated. Areal bone mineral density (aBMD) assessed by dual-energy X-ray absorptiometry (DXA) is the gold-standard method for osteoporosis diagnosis and hence also for future fracture prediction (prognostic). However, the required special equipment is not broadly available everywhere, in particular not to patients in developi…
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）
In total hip arthroplasty, analysis of postoperative medical images is important to evaluate surgical outcome. Since Computed Tomography (CT) is most prevalent modality in orthopedic surgery, we aimed at the analysis of CT image. In this work, we focus on the metal artifact in postoperative CT caused by the metallic implant, which reduces the accuracy of segmentation especially in the vicinity of the implant. Our goal was to develop an automated segmentation method of the bones and muscles in the postoperative CT images. We propose a method that combines Normalized Metal Artifact Reduction (NM…
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）
School of Electrical & Computer Engineering, University of Tehran（德黑兰大学电气与计算机工程学院）；Graduate School of Information Science, Nara Institute of Science and Technology（奈良科学技术研究院信息科学研究生院）；Osaka University Graduate School of Medicine（大阪大学医学院研究生院）
Alignment of the bones in standing position provides useful information in surgical planning. In total hip arthroplasty (THA), pelvic sagittal inclination (PSI) angle in the standing position is an important factor in planning of cup alignment and has been estimated mainly from radiographs. Previous methods for PSI estimation used a patient-specific CT to create digitally reconstructed radiographs (DRRs) and compare them with the radiograph to estimate relative position between the pelvis and the x-ray detector. In this study, we developed a method that estimates PSI angle from a single antero…
University of Tabriz（大不里士大学）；University of Tehran（德黑兰大学）；Tehran University of Medical Sciences（德黑兰医科大学）
Background and Objective: The Acetabular version, an essential factor in total hip arthroplasty, is measured by CT scan as the gold standard. The dose of radiation and expensiveness of CT make anterior-posterior pelvic radiograph an appropriate alternative procedure. In this study, we applied a deep learning approach on anteroposterior pelvic X-rays to measure anatomical version, eliminating the necessity of using Computed tomography scan. Methods: The right and left acetabular version angles of the hips of 300 patients are computed using their CT images. The proposed deep learning model, Atte…
Comp2Comp: Open-Source Software with FDA-Cleared Artificial Intelligence Algorithms for Computed Tomography Image Analysis
Stanford Mussallem Center for Biodesign（斯坦福穆萨勒姆生物设计中心）；Stanford Center for Artificial Intelligence in Medicine and Imaging（斯坦福医学与影像人工智能中心）；Stanford Department of Radiology（斯坦福放射科）；Stanford Division of Vascular Surgery, Department of Surgery（斯坦福血管外科 division，外科部）；University of Mississippi Medical Center Department of Data Science（密苏里大学医学中心数据科学部）
Concordia University（康考迪亚大学）；University of Southern California（南加州大学）
Accurate segmentation of the pelvic CTs is crucial for the clinical diagnosis of pelvic bone diseases and for planning patient-specific hip surgeries. With the emergence and advancements of deep learning for digital healthcare, several methodologies have been proposed for such segmentation tasks. But in a low data scenario, the lack of abundant data needed to train a Deep Neural Network is a significant bottle-neck. In this work, we propose a methodology based on modulation of image tonal distributions and deep learning to boost the performance of networks trained on limited data. The strategy…
University of Rochester（罗切斯特大学）；Edmund A. Hajim School of Engineering and Applied Sciences（埃德蒙·A·哈吉姆工程与应用科学学院）
Bone fragility and fracture caused by osteoporosis or injury are prevalent in adults over the age of 50 and can reduce their quality of life. Hence, predicting the biomechanical bone strength, specifically of the proximal femur, through non-invasive imaging-based methods is an important goal for the diagnosis of Osteoporosis as well as estimating fracture risk. Dual X-ray absorptiometry (DXA) has been used as a standard clinical procedure for assessment and diagnosis of bone strength and osteoporosis through bone mineral density (BMD) measurements. However, previous studies have shown that qua…
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）
