Object detection methods are widely adopted for computer-aided diagnosis using medical images. Anomalous findings are usually treated as objects that are described by bounding boxes. Yet, many pathological findings, e.g., bone fractures, cannot be clearly defined by bounding boxes, owing to considerable instance, shape and boundary ambiguities. This makes bounding box annotations, and their associated losses, highly ill-suited. In this work, we propose a new bone fracture detection method for X-ray images, based on a labor effective and flexible annotation scheme suitable for abnormal findings with no clear object-level spatial extents or boundaries. Our method employs a simple, intuitive, and informative point-based annotation protocol to mark localized pathology information. To address the uncertainty in the fracture scales annotated via point(s), we convert the annotations into pixel-wise supervision that uses lower and upper bounds with positive, negative, and uncertain regions. A novel Window Loss is subsequently proposed to only penalize the predictions outside of the uncertain regions. Our method has been extensively evaluated on 4410 pelvic X-ray images of unique patients. Experiments demonstrate that our method outperforms previous state-of-the-art image classification and object detection baselines by healthy margins, with an AUROC of 0.983 and FROC score of 89.6%.
Two line-based fracture detection scheme are developed and discussed, namely Standard line-based fracture detection and Adaptive Differential Parameter Optimized (ADPO) line-based fracture detection. The purpose for the two line-based fracture detection schemes is to detect fractured lines from X-ray images using extracted features based on recognised patterns to differentiate fractured lines from non-fractured lines. The difference between the two schemes is the detection of detailed lines. The ADPO scheme optimizes the parameters of the Probabilistic Hough Transform, such that granule lines …
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；LEAD Technologies, Inc.（LEAD科技公司）
High energy impacts at joint locations often generate highly fragmented, or comminuted, bone fractures. Current approaches for treatment require physicians to decide how to classify the fracture within a hierarchy fracture severity categories. Each category then provides a best-practice treatment scenario to obtain the best possible prognosis for the patient. This article identifies shortcomings associated with qualitative-only evaluation of fracture severity and provides new quantitative metrics that serve to address these shortcomings. We propose a system to semi-automatically extract quanti…
The thighbone is the largest bone supporting the lower body. If the thighbone fracture is not treated in time, it will lead to lifelong inability to walk. Correct diagnosis of thighbone disease is very important in orthopedic medicine. Deep learning is promoting the development of fracture detection technology. However, the existing computer aided diagnosis (CAD) methods baesd on deep learning rely on a large number of manually labeled data, and labeling these data costs a lot of time and energy. Therefore, we develop a object detection method with limited labeled image quantity and apply it t…
Fracture interactive geodesic active contours for bone segmentation
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China（数学科学国家重点实验室，数学与系统科学研究院，中国科学院，北京）；University of Chinese Academy of Sciences, Beijing 100190, China（中国科学院大学，北京）；Department of Orthopedics, The Fourth Medical Center of Chinese PLA General Hospital, Beijing 100048, China（中国人民解放军总医院第四医学中心骨科，北京）；National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, Beijing 100048, China（骨科、运动医学与康复国家临床医学研究中心，北京）；Department of Trauma and Orthopedics, People’s Hospital Peking University, Beijing 100044, China（北京大学人民医院创伤与骨科，北京）
School of Electrical and Information Engineering（电气与信息工程学院）；University of the Witwatersrand（金山大学）
The following paper proposes two contour-based fracture detection schemes. The development of the contour-based fracture is based on the line-based fracture detection schemes proposed in arXiv:1902.07458. Existing Computer Aided Diagnosis (CAD) systems commonly employs Convolutional Neural Networks (CNN), although the cost to obtain a high accuracy is the amount of training data required. The purpose of the proposed schemes is to obtain a high classification accuracy with a reduced number of training data through the use of detected contours in X-ray images. There are two contour-based fractur…
University of Minnesota（明尼苏达大学）；Wayzata High School（韦扎塔高中）
Distinguishing agents of bone modification at paleoanthropological sites is at the root of much of the research directed at understanding early hominin exploitation of large animal resources and the effects those subsistence behaviors had on early hominin evolution. However, current methods, particularly in the area of fracture pattern analysis as a signal of marrow exploitation, have failed to overcome equifinality. Furthermore, researchers debate the replicability and validity of current and emerging methods for analyzing bone modifications. Here we present a new approach to fracture pattern…
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Hospital（约翰斯·霍普金斯医院）；Technische Universität München（慕尼黑工业大学）
In unilateral pelvic fracture reductions, surgeons attempt to reconstruct the bone fragments such that bilateral symmetry in the bony anatomy is restored. We propose to exploit this "structurally symmetric" nature of the pelvic bone, and provide intra-operative image augmentation to assist the surgeon in repairing dislocated fragments. The main challenge is to automatically estimate the desired plane of symmetry within the patient's pre-operative CT. We propose to estimate this plane using a non-linear optimization strategy, by minimizing Tukey's biweight robust estimator, relying on the parti…
A Modified VGG19-Based Framework for Accurate and Interpretable Real-Time Bone Fracture Detection
East West University（东西大学）；Islamic University of Technology（伊斯兰技术大学）；Ahsanullah University of Science and Technology（阿赫萨努拉科技大学）；Bangladesh Agricultural University（孟加拉国农业大学）；Brac University（布拉克大学）
Masked Registration and Autoencoding of CT Images for Predictive Tibia Reconstruction
Technical University Berlin（技术大学柏林）；Charité – Universitätsmedizin Berlin（柏林查理医院）；Freie Universität Berlin（自由大学柏林）；Humboldt-Universität zu Berlin（洪堡大学柏林）；Institute of Medical Informatics（医学信息学研究所）；Center for Musculoskeletal Surgery（骨科手术中心）；Julius Wolff Institute（朱利叶斯·沃尔夫研究所）
A multi-stage semi-supervised learning for ankle fracture classification on CT images
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Biomedical Engineering, Southern Medical University（南方医科大学生物医学工程学院）
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…
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
University of Delaware（特拉华大学）；Amazon Web Services（亚马逊云科技）；University of Rochester（罗切斯特大学）
Objective and Impact Statement. We adopt a deep learning model for bone osteolysis prediction on computed tomography (CT) images of murine breast cancer bone metastases. Given the bone CT scans at previous time steps, the model incorporates the bone-cancer interactions learned from the sequential images and generates future CT images. Its ability of predicting the development of bone lesions in cancer-invading bones can assist in assessing the risk of impending fractures and choosing proper treatments in breast cancer bone metastasis. Introduction. Breast cancer often metastasizes to bone, cau…
Beijing Institute of Technology（北京理工大学）；The First Affiliated Hospital of Jinzhou Medical University（锦州医科大学附属第一医院）；Zhejiang University of Science & Technology（浙江科技学院）；Peking University People’s Hospital（北京大学人民医院）
Calcaneus is the largest tarsal bone to withstand the daily stresses of weight-bearing. The calcaneal fracture is the most common type in the tarsal bone fractures. After a fracture is suspected, plain radiographs should be taken first. Bohler's Angle (BA) and Critical Angle of Gissane (CAG), measured by four anatomic landmarks in lateral foot radiograph, can guide fracture diagnosis and facilitate operative recovery of the fractured calcaneus. This study aims to develop an analysis system that can automatically locate four anatomic landmarks, measure BA and CAG for fracture assessment, identi…
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Hospital（约翰斯·霍普金斯医院）；Technical University of Munich（慕尼黑工业大学）
We present a novel methodology to detect imperfect bilateral symmetry in CT of human anatomy. In this paper, the structurally symmetric nature of the pelvic bone is explored and is used to provide interventional image augmentation for treatment of unilateral fractures in patients with traumatic injuries. The mathematical basis of our solution is on the incorporation of attributes and characteristics that satisfy the properties of intrinsic and extrinsic symmetry and are robust to outliers. In the first step, feature points that satisfy intrinsic symmetry are automatically detected in the Möbiu…
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…
George Mason University（乔治梅森大学）；Beijing Electronic Science and Technology Institute（北京电子科技学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Beijing Jishuitan Hospital（北京积水潭医院）
Purpose: Pelvic bone segmentation in CT has always been an essential step in clinical diagnosis and surgery planning of pelvic bone diseases. Existing methods for pelvic bone segmentation are either hand-crafted or semi-automatic and achieve limited accuracy when dealing with image appearance variations due to the multi-site domain shift, the presence of contrasted vessels, coprolith and chyme, bone fractures, low dose, metal artifacts, etc. Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored. Methods: In this paper, we aim to bridg…
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…
Michigan Technological University（密歇根理工大学）；University of California, Irvine（加州大学欧文分校）；Mayo Clinic（梅奥诊所）；Tulane University（杜兰大学）；University of Southern Mississippi（南密西西比大学）
Purpose: Proximal femur image analyses based on quantitative computed tomography (QCT) provide a method to quantify the bone density and evaluate osteoporosis and risk of fracture. We aim to develop a deep-learning-based method for automatic proximal femur segmentation. Methods and Materials: We developed a 3D image segmentation method based on V-Net, an end-to-end fully convolutional neural network (CNN), to extract the proximal femur QCT images automatically. The proposed V-net methodology adopts a compound loss function, which includes a Dice loss and a L2 regularizer. We performed experime…
We introduce the deep network trained on the MURA dataset from the Stanford University released in 2017. Our system is able to detect bone abnormalities on the radiographs and visualise such zones. We found that our solution has the accuracy comparable to the best results that have been achieved by other development teams that used MURA dataset, in particular the overall Kappa score that was achieved by our team is about 0.942 on the wrist, 0.862 on the hand and o.735 on the shoulder (compared to the best available results to this moment on the official web-site 0.931, 0.851 and 0.729 accordin…
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）
Zero-shot large vision-language model prompting for automated bone identification in paleoradiology x-ray archives
Vanderbilt University（范德堡大学）；Vanderbilt University School of Medicine（范德堡大学医学院）；Vanderbilt University Medical Center（范德堡大学医学中心）；Museum of London（伦敦博物馆）；Ossafreelance
Sylhet Engineering College（锡尔赫特工程学院）；Shahjalal University of Science and Technology（沙赫贾拉尔科技大学）
Classification of skull fracture is a challenging task for both radiologists and researchers. Skull fractures result in broken pieces of bone, which can cut into the brain and cause bleeding and other injury types. So it is vital to detect and classify the fracture very early. In real world, often fractures occur at multiple sites. This makes it harder to detect the fracture type where many fracture types might summarize a skull fracture. Unfortunately, manual detection of skull fracture and the classification process is time-consuming, threatening a patient's life. Because of the emergence of…
PAII Inc.（PAII公司）；University of Maryland（马里兰大学）；Chang Gung Memorial Hospital（长庚纪念医院）；Ping An Technology（平安科技）
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-su…
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）
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…
Elbow fractures are one of the most common fracture types. Diagnoses on elbow fractures often need the help of radiographic imaging to be read and analyzed by a specialized radiologist with years of training. Thanks to the recent advances of deep learning, a model that can classify and detect different types of bone fractures needs only hours of training and has shown promising results. However, most existing deep learning models are purely data-driven, lacking incorporation of known domain knowledge from human experts. In this work, we propose a novel deep learning method to diagnose elbow fr…
Cross-Source Supervision for Bone Infection Segmentation in Dual-Modality PET-CT
Shanghai Maritime University（上海海洋大学）；University College London（伦敦大学学院）；Shanghai Sixth People’s Hospital（上海第六人民医院）；Shanghai Sixth People’s Hospital Affiliated to SJTU School of Medicine（上海第六人民医院附属复旦大学医学院）；Queen Mary University of London（伦敦女王玛丽大学）
PF-DAformer: Proximal Femur Segmentation via Domain Adaptive Transformer for Dual-Center QCT
Michigan Technological University（密歇根理工大学）；Kennesaw State University（肯尼索州立大学）；University of California, Irvine（加利福尼亚大学尔湾分校）；Tulane University School of Public Health and Tropical Medicine（杜兰大学公共卫生与热带医学院）
Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge
Beijing Rossum Robot Technology Co., Ltd.（北京罗素机器人科技有限公司）；Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University（生物力学与机械生物学重点实验室，教育部，北京生物医学创新中心，生物科学与医学工程学院，北航）；Department of Computer Science, Johns Hopkins University（计算机科学系，约翰霍普金斯大学）；Division of Medical Image Computing, German Cancer Research Center (DKFZ)（医学影像计算部，德国癌症研究中心（DKFZ））；Helmholtz Imaging, Heidelberg（海德堡大学医院影像中心）；Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Medical UltraSound Image Computing（智能医学影像、学习与工程（SMILE）实验室，医学超声影像计算）
New York University School of Medicine（纽约大学医学院）；Center for Data Science, New York University（纽约大学数据科学中心）；Harvard College（哈佛学院）；Hospital for Joint Diseases, New York University Langone Medical Center（纽约大学朗格尼医学中心关节疾病医院）；Courant Institute of Mathematical Science, New York University（纽约大学库朗数学科学研究所）
Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality and assess fracture risk. However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical practice. The purpose of this paper is to present an automatic proximal femur segmentation method that is based on deep convolutional neural networks (CNNs). This study had institutional review board approval and written informed consent was obtained from all subjects. A dataset of volumetric structural MR images of the proximal femur from 86 subjec…
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）
Computer-Aided Osteoporosis Diagnosis Using Transfer Learning with Enhanced Features from Stacked Deep Learning Modules
Bangladesh Army University of Science and Technology（孟加拉国陆军科技大学）；Brac University（BRAC大学）；National Institute of Textile Engineering and Research (NITER)（国家纺织工程与研究所（NITER））；University of Dhaka（达卡大学）
