Automatic spinal curvature measurement on ultrasound spine images using Faster R-CNN
ShanghaiTech University（上海科技大学）；University of Alberta（阿尔伯塔大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）
Ultrasound spine imaging technique has been applied to the assessment of spine deformity. However, manual measurements of scoliotic angles on ultrasound images are time-consuming and heavily rely on raters experience. The objectives of this study are to construct a fully automatic framework based on Faster R-CNN for detecting vertebral lamina and to measure the fitting spinal curves from the detected lamina pairs. The framework consisted of two closely linked modules: 1) the lamina detector for identifying and locating each lamina pairs on ultrasound coronal images, and 2) the spinal curvature estimator for calculating the scoliotic angles based on the chain of detected lamina. Two hundred ultrasound images obtained from AIS patients were identified and used for the training and evaluation of the proposed method. The experimental results showed the 0.76 AP on the test set, and the Mean Absolute Difference (MAD) between automatic and manual measurement which was within the clinical acceptance error. Meanwhile the correlation between automatic measurement and Cobb angle from radiographs was 0.79. The results revealed that our proposed technique could provide accurate and reliable automatic curvature measurements on ultrasound spine images for spine deformities.
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所智能信息处理重点实验室）；Suzhou Institute of Intelligent Computing Technology, Chinese Academy of Sciences（中国科学院苏州智能计算技术研究院）；Beijing Jishuitan Hospital（北京积水潭医院）；School of Medicine, South China University of Technology（华南理工大学医学院）
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）
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 id…
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）
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河右岸医院）；Graz University of Technology（格拉茨工业大学）；University of Auckland（奥克兰大学）
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural weight bearing is crucial for such bio-mechanical analysis. 3D volumetric imaging modalities (e.g. CT and MRI) are performed in patients lying down. On the other hand, radiographs are captured in an upright pose, but result in 2D projections. This work aims to integrate the two realms, i.e. it combines the upright spinal curvature from radiographs with the 3D vertebral shape from CT imaging for synthesizing an uprigh…
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…
ShanghaiTech University（上海科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；University of Alberta（阿尔伯塔大学）
Objective: The spinous process angle (SPA) is one of the essential parameters to denote three-dimensional (3-D) deformity of spine. We propose an automatic segmentation method based on Stacked Hourglass Network (SHN) to detect the spinous processes (SP) on ultrasound (US) spine images and to measure the SPAs of clinical scoliotic subjects. Methods: The network was trained to detect vertebral SP and laminae as five landmarks on 1200 ultrasound transverse images and validated on 100 images. All the processed transverse images with highlighted SP and laminae were reconstructed into a 3D image vol…
Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI
A. K. Choudhury School of Information Technology, University of Calcutta（加尔各答大学A.K.乔杜里信息技术学院）；Rammohan College, University of Calcutta（加尔各答大学拉莫汉学院）；Indian Statistical Institute（印度统计研究院）
SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI
NeuroPoly, Institute of Biomedical Engineering, Polytechnique Montreal（NeuroPoly，生物医学工程研究所，蒙特利尔大学）；Mila, Quebec AI Institute, Montreal（Mila，魁北克人工智能研究所，蒙特利尔）；Department of Radiology, Balgrist University Hospital, Zurich（放射科，巴尔格里斯大学医院，苏黎世）；Faculty of Medicine, University of Zurich（医学学院，苏黎世大学）；Department of Neurosurgery, Brain & Spine, University of South Florida（神经外科，脑与脊柱，佛罗里达大学）；Center for Neuromusculoskeletal Research, University of South Florida（神经肌肉研究中心，佛罗里达大学）；Department of Medical Engineering, University of South Florida（医学工程系，佛罗里达大学）；Stanford School of Medicine, Stanford（斯坦福医学院，斯坦福）；Functional Neuroimaging Unit, CRIUGM, Université de Montréal（功能神经影像单元，CRIUGM，蒙特利尔大学）；Centre de recherche du CHU Sainte-Justine, Université de Montréal（圣若泽中心研究，蒙特利尔大学）
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）
SpineMamba: Enhancing 3D Spinal Segmentation in Clinical Imaging through Residual Visual Mamba Layers and Shape Priors
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Northeast Petroleum University（东北石油大学）；Northeastern University（东北大学）；Southern Medical University（南方医科大学）
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 …
Shandong University（山东大学）；Shandong University of Traditional Chinese Medicine（山东中医药大学）；Western University（韦仕敦大学）
Automated medical report generation in spine radiology, i.e., given spinal medical images and directly create radiologist-level diagnosis reports to support clinical decision making, is a novel yet fundamental study in the domain of artificial intelligence in healthcare. However, it is incredibly challenging because it is an extremely complicated task that involves visual perception and high-level reasoning processes. In this paper, we propose the neural-symbolic learning (NSL) framework that performs human-like learning by unifying deep neural learning and symbolic logical reasoning for the s…
Insight Centre for Data Analytics, Dublin City University（都柏林城市大学数据洞察中心）；Sports Medicine Department, Sports Surgery Clinic（运动外科诊所运动医学部）；School of Health and Human Performance, Dublin City University（都柏林城市大学健康与人体运动学院）
In this paper we present an interactive tool that can be used to quantify fat infiltration in lumbar muscles, which is useful in studying fat infiltration and lower back pain (LBP) in adults. Currently, a qualitative assessment by visual grading via a 5-point scale is used to study fat infiltration in lumbar muscles from an axial view of lumbar-spine MR Images. However, a quantitative approach (on a continuous scale of 0-100\%) may provide a greater insight. In this paper, we propose a method to precisely quantify the fat deposition / infiltration in a user-defined region of the lumbar muscles…
Brigham and Women’s Hospital（布列根和妇女医院）；Massachusetts General Hospital（麻省总医院）
The high prevalence of spinal stenosis results in a large volume of MRI imaging, yet interpretation can be time-consuming with high inter-reader variability even among the most specialized radiologists. In this paper, we develop an efficient methodology to leverage the subject-matter-expertise stored in large-scale archival reporting and image data for a deep-learning approach to fully-automated lumbar spinal stenosis grading. Specifically, we introduce three major contributions: (1) a natural-language-processing scheme to extract level-by-level ground-truth labels from free-text radiology rep…
SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河右岸医院）；University of Zurich（苏黎世大学）；Imperial College London（帝国理工学院）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；University Medical Center Rostock（罗斯托克大学医学中心）；University of Freiburg（弗赖堡大学）；University Hospital Augsburg（奥格斯堡大学医院）；Augsburg University（奥格斯堡大学）
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…
Technische Universität München（慕尼黑工业大学）；ScintHealth GmbH（ScintHealth有限公司）；Reply SpA（Reply股份公司）；Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；Helmholtz AI（亥姆霍兹人工智能研究所）
CT and MRI are two of the most informative modalities in spinal diagnostics and treatment planning. CT is useful when analysing bony structures, while MRI gives information about the soft tissue. Thus, fusing the information of both modalities can be very beneficial. Registration is the first step for this fusion. While the soft tissues around the vertebra are deformable, each vertebral body is constrained to move rigidly. We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. To achi…
The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
University of Utah School of Medicine（犹他大学医学院）；Thomas Jefferson University（托马斯·杰斐逊大学）；St. Michael’s Hospital（圣迈克尔医院）；Unity Health Toronto（多伦多联合医疗）；The Ohio State University（俄亥俄州立大学）；The Jackson Laboratory（杰克逊实验室）；Bunkerhilll Health（邦克希尔健康公司）；Universidade Federal de São Paulo（圣保罗联邦大学）；University of California San Francisco（加州大学旧金山分校）；Radiological Society of North America（北美放射学会）；Chiang Mai University Faculty of Medicine（清迈大学医学院）；Koc University School of Medicine（科奇大学医学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Diagnósticos da América S.A.（美洲诊断公司）；Gold Coast University Hospital（黄金海岸大学医院）；Clinical Center University of Sarajevo（萨拉热窝大学临床中心）；University Hospitals Cleveland Medical Center（克利夫兰大学医院医疗中心）；Tallaght University Hospital（塔拉特大学医院）；Kingston Health Sciences Centre（金斯顿健康科学中心）；Queen’s University（女王大学）；Loyola University Medical Center（洛约拉大学医学中心）；George Washington University Hospital（乔治华盛顿大学医院）；Children’s National Hospital（国家儿童医院）；Scripps Clinical Medical Group（斯克里普斯临床医疗集团）
Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
Department of Diagnostic and Interventional Neuroradiology, School of Medicine, TUM University Hospital, Technical University of Munich, München, Germany（诊断与介入神经放射科，医学院，TUM大学医院，慕尼黑技术大学，慕尼黑，德国）；Institut für KI und Informatik in der Medizin, TUM University Hospital, Technical University of Munich, München, Germany（医学人工智能与医学信息学研究所，TUM大学医院，慕尼黑技术大学，慕尼黑，德国）；Sports Orthopedics Department, Klinikum Rechts der Isar, Technical University of Munich, München, Germany（运动骨科部门，Rechts der Isar诊所，慕尼黑技术大学，慕尼黑，德国）；Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany（神经科，医学院，慕尼黑技术大学，慕尼黑，德国）；Department of Radiology, Weill Cornell Medicine, New York, USA（放射科，韦尔·科恩医学中心，纽约，美国）；Department of Computing, Imperial College London, London, UK（计算系，伦敦帝国理工学院，伦敦，英国）
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Medical Institute（约翰斯·霍普金斯医学研究所）
Clear identification of bone structures is crucial for ultrasound-guided lumbar interventions, but it can be challenging due to the complex shapes of the self-shadowing vertebra anatomy and the extensive background speckle noise from the surrounding soft tissue structures. Therefore, we propose to use a patch-like wearable ultrasound solution to capture the reflective bone surfaces from multiple imaging angles and create 3D bone representations for interventional guidance. In this work, we will present our method for estimating the vertebra bone surfaces by using a spatiotemporal U-Net archite…
Technische Universität München（慕尼黑工业大学）；The Hong Kong Polytechnic University（香港理工大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Computer Aided Medical Procedures（计算机辅助医疗程序（注意：该机构名可能属于前序大学/学院下的研究方向，此处按原文提取，若机构名本身为研究方向而非独立机构，需按要求忽略，但根据文本格式，其前接大学名，此处可能需保留））
Spine injections are commonly performed in several clinical procedures. The localization of the target vertebral level (i.e. the position of a vertebra in a spine) is typically done by back palpation or under X-ray guidance, yielding either higher chances of procedure failure or exposure to ionizing radiation. Preliminary studies have been conducted in the literature, suggesting that ultrasound imaging may be a precise and safe alternative to X-ray for spine level detection. However, ultrasound data are noisy and complicated to interpret. In this study, a robotic-ultrasound approach for automa…
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）
MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset
School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.（南方医科大学生物医学工程学院）；Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, 510515, China.（广东省医学图像处理重点实验室）；Guangdong Province Engineering Laboratory for Medical Imaging（广东省医学影像与诊断技术工程实验室）；Information Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.（南方医科大学南华医院信息中心）；Division of Spine Surgery, Department of Orthopaedics, Nanfang hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.（南方医科大学南华医院骨科脊柱外科）
A multi-agent system for spine MRI report generation from multi-sequence imaging
University of Washington（华盛顿大学）；Peking University（北京大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；New York University（纽约大学）；University of Washington Medical Center（华盛顿大学医学中心）
The Hong Kong Polytechnic University（香港理工大学）；University of Technology Sydney（悉尼科技大学）；Harbin Engineering University（哈尔滨工程大学）；Department of Electronic and Information Engineering, The Hong Kong Polytechnic University（电子与信息工程系，香港理工大学）；School of Biomedical Engineering, University of Technology Sydney（生物医学工程学院，悉尼科技大学）；Department of Biomedical Engineering, The Hong Kong Polytechnic University（生物医学工程系，香港理工大学）；College of Information and Communication Engineering, Harbin Engineering University（信息与通信工程学院，哈尔滨工程大学）
3D ultrasound imaging shows great promise for scoliosis diagnosis thanks to its low-costing, radiation-free and real-time characteristics. The key to accessing scoliosis by ultrasound imaging is to accurately segment the bone area and measure the scoliosis degree based on the symmetry of the bone features. The ultrasound images tend to contain many speckles and regular occlusion noise which is difficult, tedious and time-consuming for experts to find out the bony feature. In this paper, we propose a robust bone feature segmentation method based on the U-net structure for ultrasound spine Volum…
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling
University of Regensburg（雷根斯堡大学）；South Dakota State University（南达科他州立大学）；RWTH Aachen University（亚琛工业大学）；Northwestern University（西北大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所）
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…
Automatic segmentation of vertebral bodies (VBs) and intervertebral discs (IVDs) in 3D magnetic resonance (MR) images is vital in diagnosing and treating spinal diseases. However, segmenting the VBs and IVDs simultaneously is not trivial. Moreover, problems exist, including blurry segmentation caused by anisotropy resolution, high computational cost, inter-class similarity and intra-class variability, and data imbalances. We proposed a two-stage algorithm, named semi-supervised hybrid spine network (SSHSNet), to address these problems by achieving accurate simultaneous VB and IVD segmentation.…
Background: Magnetic field inhomogeneities generate important geometric distortions in reconstructed echo-planar images. Various procedures were proposed for correcting these distortions on brain images; yet, few neuroimaging studies tailored and incorporated the use of these techniques in spinal cord diffusion MRI. Purpose: We present a comparative evaluation of distortion correction methods that use the reversed gradient polarity technique on spinal cord. We propose novel geometric metrics to measure the alignment of the reconstructed diffusion model with the apparent centerline of the spina…
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）
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 …
Technische Universität München（慕尼黑工业大学）；Sharif University of Technology（谢里夫理工大学）；John Hopkins University（约翰霍普金斯大学）
Medical Ultrasound (US), despite its wide use, is characterized by artifacts and operator dependency. Those attributes hinder the gathering and utilization of US datasets for the training of Deep Neural Networks used for Computer-Assisted Intervention Systems. Data augmentation is commonly used to enhance model generalization and performance. However, common data augmentation techniques, such as affine transformations do not align with the physics of US and, when used carelessly can lead to unrealistic US images. To this end, we propose a set of physics-inspired transformations, including defo…
T2 weighted imaging of the spine is commonly performed using fast spin echo (FSE/TSE) based sequences, resulting in long scan times and vulnerability to motion artifacts. While single shot fast spin echo sequences have been attempted, their adoption has been limited by poor spatial resolution and specific absorption rate (SAR) limitations. We investigate the use of a half-Fourier acquisition single-shot turbo spin-echo variable flip angle (HASTE-VFA) sequence for T2 weighted spine imaging. A variable refocusing flip angle echo train was first optimized for the spine to improve the point spread…
Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海高等研究院）；University of Chinese Academy of Sciences（中国科学院大学）；College of Computer Science, Sichuan University（四川大学计算机学院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；The Chinese University of Hong Kong（香港中文大学）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝斯以色列女执事医疗中心）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）
Helmholtz Zentrum Muenchen（慕尼黑亥姆霍兹中心）；Technische Universitaet Muenchen（慕尼黑工业大学）；Reply SpA（瑞普公司）；Johns Hopkins University（约翰斯·霍普金斯大学）
Symptomatic spinal vertebral compression fractures (VCFs) often require osteoplasty treatment. A cement-like material is injected into the bone to stabilize the fracture, restore the vertebral body height and alleviate pain. Leakage is a common complication and may occur due to too much cement being injected. In this work, we propose an automated patient-specific framework that can allow physicians to calculate an upper bound of cement for the injection and estimate the optimal outcome of osteoplasty. The framework uses the patient CT scan and the fractured vertebra label to build a virtual he…
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（右伊萨尔医院）；University of Zurich（苏黎世大学）；Institut für KI und Informatik in der Medizin（医学人工智能与信息学研究所）；Imperial College London（伦敦帝国学院）
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）
