University of California, Los Angeles（加州大学洛杉矶分校）；Tencent Hippocrates Research Lab（腾讯希波克拉底研究实验室）；The University of Hong Kong（香港大学）；VoxelCloud, Inc.（VoxelCloud公司）
Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang, Wei Fan, Kenneth M. C. Cheung, Michael To, Zhen Qian, Demetri Terzopoulos
Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Measurement of scoliosis requires labeling and identification of vertebrae in the spine. Spine radiographs are the most cost-effective and accessible modality for imaging the spine. Reliable and accurate vertebrae segmentation in spine radiographs is crucial in image-guided spinal assessment, disease diagnosis, and treatment planning. Conventional assessments rely on tedious and time-consuming manual measurement, which is subject to inter-observer variability. A fully automatic method that can accurately identify and segment the associated vertebrae is unavailable in the literature. Leveraging a carefully-adjusted U-Net model with progressive side outputs, we propose an end-to-end segmentation model that provides a fully automatic and reliable segmentation of the vertebrae associated with scoliosis measurement. Our experimental results from a set of anterior-posterior spine X-Ray images indicate that our model, which achieves an average Dice score of 0.993, promises to be an effective tool in the identification and labeling of spinal vertebrae, eventually helping doctors in the reliable estimation of scoliosis. Moreover, estimation of Cobb angles from the segmented vertebrae further demonstrates the effectiveness of our model.
ViT3Flow: A Test-Time Training Transformer MeanFlow for Postoperative Radiograph Synthesis in Scoliosis
The University of Hong Kong（香港大学）；The University of Hong Kong-Shenzhen Hospital（香港大学深圳医院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）
University of Science and Technology of Hanoi（河内理工大学）
Scoliosis is a sideways curvature of the spine that most often is diagnosed among young teenagers. It dramatically affects the quality of life, which can cause complications from heart and lung injuries in severe cases. The current gold standard to detect and estimate scoliosis is to manually examine the spinal anterior-posterior X-ray images. This process is time-consuming, observer-dependent, and has high inter-rater variability. Consequently, there has been increasing interest in automatic scoliosis estimation from spinal X-ray images, and the development of deep learning has shown amazing …
The Hong Kong Polytechnic University（香港理工大学）；Research Institute for Smart Ageing, PolyU（香港理工大学智衰研究所）
The scoliosis progression in adolescents requires close monitoring to timely take treatment measures. Ultrasound imaging is a radiation-free and low-cost alternative in scoliosis assessment to X-rays, which are typically used in clinical practice. However, ultrasound images are prone to speckle noises, making it challenging for sonographers to detect bony features and follow the spine's curvature. This paper introduces a robotic-ultrasound approach for spinal curvature tracking and automatic navigation. A fully connected network with deconvolutional heads is developed to locate the spinous pro…
Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；Ecole des Mines de Saint-Etienne（圣艾蒂安国立矿业学院）；Ecole Centrale Marseille（马赛中央理工学院）；TU Delft（代尔夫特理工大学）
Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the estimation of Cobb angles from X-ray scans. First, the centerline of the spine was segmented using a cascade of two convolutional neural networks. After smoothing the centerline, Cobb angles were automatically estimated using the derivative of the centerline. We evaluated the results using the mean absolute error and the average symmetric mean absolute percentage error between the manual …
Accurate Cobb Angle Estimation via SVD-Based Curve Detection and Vertebral Wedging Quantification
Department of Orthopaedics and Traumatology, LKS Faculty of Medicine, The University of Hong Kong（骨科与创伤学系，李嘉诚医学院，香港大学）；Department of Spine Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（脊柱外科科，仁济医院，医学院，上海交通大学）
Intelligent Scoliosis Screening and Diagnosis: A Survey
University of Electronic Science and Technology of China（电子科技大学）；Chengdu Chengdian Jinpan Health Data Technology Limited Company（成都成电金盘健康数据技术有限公司）；Hospital of Chengdu University of TCM（成都中医药大学附属医院）；Zigong First People’s Hospital（自贡市第一人民医院）
Reconstruction of 3D lumbar spine models from incomplete segmentations using landmark detection
University of Koblenz（科布伦茨大学）；Institute for Computational Visualistics（计算视觉研究所）；Institute for Medical Technology and Information Processing（医学技术与信息处理研究所）
R2MF-Net: A Recurrent Residual Multi-Path Fusion Network for Robust Multi-directional Spine X-ray Segmentation
School of Information Science & Engineering, Shandong Normal University（信息科学与工程学院，山东师范大学）；Tajik State University of Law, Business and Politics（塔吉克国立法律、商业与政治大学）
Vingroup Big Data Institute（Vingroup大数据研究所）；Hanoi University of Science and Technology（河内理工大学）；VinUniversity（Vin大学）；Yale University（耶鲁大学）
Radiographs are used as the most important imaging tool for identifying spine anomalies in clinical practice. The evaluation of spinal bone lesions, however, is a challenging task for radiologists. This work aims at developing and evaluating a deep learning-based framework, named VinDr-SpineXR, for the classification and localization of abnormalities from spine X-rays. First, we build a large dataset, comprising 10,468 spine X-ray images from 5,000 studies, each of which is manually annotated by an experienced radiologist with bounding boxes around abnormal findings in 13 categories. Using thi…
Tencent Medical AI Lab（腾讯医疗人工智能实验室）；University of California, Los Angeles（加州大学洛杉矶分校）；The University of Hong Kong（香港大学）；VoxelCloud, Inc.（体素科技有限公司）
Scoliosis is a congenital disease that causes lateral curvature in the spine. Its assessment relies on the identification and localization of vertebrae in spinal X-ray images, conventionally via tedious and time-consuming manual radiographic procedures that are prone to subjectivity and observational variability. Reliability can be improved through the automatic detection and localization of spinal landmarks. To guide a CNN in the learning of spinal shape while detecting landmarks in X-ray images, we propose a novel loss based on a bipartite distance (BPD) measure, and show that it consistentl…
The establishment of static digital humans and the integration with spinal models
College of Computer Science, Beijing University of Technology（北京工业大学计算机学院）；Tsinghua University（清华大学）；Beijing University of Technology（北京工业大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Peking Union Medical College Hospital（北京协和医院）
University of Science and Technology Houari Bouemadiene USTHB（胡阿里·布迈丁科学技术大学（USTHB））；Research Center on Scientific and Technical Information, CERIST（科学技术信息研究中心（CERIST））；Specialized Hospital Bounaama Djilali, CHU Douera（杜埃拉大学医院布纳马·吉拉利专科医院）；Saad Dahleb University（萨阿德·达赫莱布大学）；Center of Development of Advanced Technologies, CDTA（先进技术发展中心（CDTA））
Children diagnosed with a scoliosis pathology are exposed during their follow up to ionic radiations in each X-rays diagnosis. This exposure can have negative effects on the patient's health and cause diseases in the adult age. In order to reduce X-rays scanning, recent systems provide diagnosis of scoliosis patients using solely RGB images. The output of such systems is a set of augmented images and scoliosis related angles. These angles, however, confuse the physicians due to their large number. Moreover, the lack of X-rays scans makes it impossible for the physician to compare RGB and X-ray…
Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from Xradiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture / non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pretrained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and …
Polytechnique Montréal（蒙特利尔理工学院）；Technopole in Pediatric Rehabilitation Engineering, Sainte-Justine UHC（圣贾斯汀大学医疗中心儿科康复工程技术中心）；Cliniques universitaires Saint-Luc（圣卢克大学医院）；Université catholique de Louvain（鲁汶天主教大学）
Objectives: To quantify the magnitude of spinal deformity in adolescent idiopathic scoliosis (AIS), the Cobb angle is measured on X-ray images of the spine. Continuous exposure to X-ray radiation to follow-up the progression of scoliosis may lead to negative side effects on patients. Furthermore, manual measurement of the Cobb angle could lead to up to 10° or more of a difference due to intra/inter observer variation. Therefore, the objective of this study is to identify the Cobb angle by developing an automated radiation-free model, using Machine learning algorithms. Methods: Thirty participa…
Automatic Ultrasound Curve Angle Measurement via Affinity Clustering for Adolescent Idiopathic Scoliosis Evaluation
The Hong Kong Polytechnic University（香港理工大学）；The Chinese University of Hong Kong（香港中文大学）；Joint Scoliosis Research Center of the Chinese University of Hong Kong and Nanjing University（香港中文大学与南京大学联合脊柱侧弯研究中心）；Nanjing University（南京大学）
Novartis Pharmaceutical Company（诺华制药公司）；Big Data Institute, University of Oxford（牛津大学大数据研究所）
Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations. In this study, we address this challenge by proposing an ensemble method called VertXNet, to automatically segment and label vertebrae in X-ray spinal images. VertXNet combines two state-of-the-art segmentation models, namely U-Net and Mask R-CNN to improve vertebrae segmentation. A main feature of VertXNet is to also infer vertebrae labels thanks to its Mask R-CNN component (trained to detect 'reference' vertebrae) on a given spinal X-ray image. VertXN…
Novartis Pharmaceuticals Company（诺华制药公司）；University of Oxford（牛津大学）；Oxford University Hospitals NHS Foundation Trust（牛津大学医院国民保健服务基金会信托）
Reliable vertebrae annotations are key to perform analysis of spinal X-ray images. However, obtaining annotation of vertebrae from those images is usually carried out manually due to its complexity (i.e. small structures with varying shape), making it a costly and tedious process. To accelerate this process, we proposed an ensemble pipeline, VertXNet, that combines two state-of-the-art (SOTA) segmentation models (respectively U-Net and Mask R-CNN) to automatically segment and label vertebrae in X-ray spinal images. Moreover, VertXNet introduces a rule-based approach that allows to robustly inf…
University of Toronto（多伦多大学）；The Hospital for Sick Children（病童医院）；Vector Institute（向量研究所）
Scoliosis is a three-dimensional deformity of the spine, most often diagnosed in childhood. It affects 2-3% of the population, which is approximately seven million people in North America. Currently, the reference standard for assessing scoliosis is based on the manual assignment of Cobb angles at the site of the curvature center. This manual process is time consuming and unreliable as it is affected by inter- and intra-observer variance. To overcome these inaccuracies, machine learning (ML) methods can be used to automate the Cobb angle measurement process. This paper proposes to address the …
German Cancer Research Center（德国癌症研究中心）；Heidelberg University（海德堡大学）
Image registration is the basis for many applications in the fields of medical image computing and computer assisted interventions. One example is the registration of 2D X-ray images with preoperative three-dimensional computed tomography (CT) images in intraoperative surgical guidance systems. Due to the high safety requirements in medical applications, estimating registration uncertainty is of a crucial importance in such a scenario. However, previously proposed methods, including classical iterative registration methods and deep learning-based methods have one characteristic in common: They…
Submillimeter-Accurate 3D Lumbar Spine Reconstruction from Biplanar X-Ray Images: Incorporating a Multi-Task Network and Landmark-Weighted Loss
School of Biomedical Engineering & Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学生物医学工程学院与Med-X研究院）；Engineering Research Center of Digital Medicine and Clinical Translation, Ministry of Education（教育部数字医学临床转化工程研究中心）；Department of Spine Surgery, Tongji Hospital, School of Medicine, Tongji University（同济大学医学院附属同济医院脊柱外科）；TAOiMAGE Medical Technologies Corporation（TAOiMAGE医疗科技有限公司）；School of Mechanical Engineering, Shanghai Jiao Tong University（上海交通大学机械工程学院）
