Expert Validation of Synthetic Cervical Spine Radiographs Generated with a Denoising Diffusion Probabilistic Model
Cumming School of Medicine, University of Calgary（卡里尔大学医学学院）；Division of Neurosurgery, Department of Clinical Neurosciences, University of Calgary（卡里尔大学临床神经科学系神经外科部）；Department of Radiology, University of Calgary（卡里尔大学放射科）；Division of Neurosurgery, Department of Surgery, University of Toronto（多伦多大学外科系神经外科部）；Department of Medical Imaging, University of Toronto（多伦多大学医学影像科）；Project neuroArm, Department of Clinical Sciences, University of Calgary（卡里尔大学临床科学部neuroArm项目）
Austin A. Barr, Brij S. Karmur, Anthony J. Winder, Eddie Guo, John T. Lysack, James N. Scott, William F. Morrish, Muneer Eesa, Morgan Willson, David W. Cadotte,… 展开作者
Austin A. Barr, Brij S. Karmur, Anthony J. Winder, Eddie Guo, John T. Lysack, James N. Scott, William F. Morrish, Muneer Eesa, Morgan Willson, David W. Cadotte, Michael M. H. Yang, Ian Y. M. Chan, Sanju Lama, Garnette R. Sutherland
Machine learning in neurosurgery is limited by challenges in assembling large, high-quality imaging datasets. Synthetic data offers a scalable, privacy-preserving solution. We evaluated the feasibility of generating realistic lateral cervical spine radiographs using a denoising diffusion probabilistic model (DDPM) trained on 4,963 images from the Cervical Spine X-ray Atlas. Model performance was monitored via training/validation loss and Frechet inception distance, and synthetic image quality was assessed in a blinded "clinical Turing test" with six neuroradiologists and two spine-fellowship trained neurosurgeons. Experts reviewed 50 quartets containing one real and three synthetic images, identifying the real image and rating realism on a 4-point Likert scale. Experts correctly identified the real image in 29% of trials (Fleiss' kappa=0.061). Mean realism scores were comparable between real (3.323) and synthetic images (3.228, 3.258, and 3.320; p=0.383, 0.471, 1.000). Nearest-neighbor analysis found no evidence of memorization. We also provide a dataset of 20,063 synthetic radiographs. These results demonstrate that DDPM-generated cervical spine X-rays are statistically indistinguishable in realism and quality from real clinical images, offering a novel approach to creating large-scale neuroimaging datasets for ML applications in landmarking, segmentation, and classification.
St. Michael’s Hospital（圣迈克尔医院）；Unity Health Toronto（多伦多统一健康中心）；University of Toronto（多伦多大学）；Stanford University（斯坦福大学）
Fractures of the cervical spine are a medical emergency and may lead to permanent paralysis and even death. Accurate diagnosis in patients with suspected fractures by computed tomography (CT) is critical to patient management. In this paper, we propose a deep convolutional neural network (DCNN) with a bidirectional long-short term memory (BLSTM) layer for the automated detection of cervical spine fractures in CT axial images. We used an annotated dataset of 3,666 CT scans (729 positive and 2,937 negative cases) to train and validate the model. The validation results show a classification accur…
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…
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）
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…
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）
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…
University of Guelph（圭尔夫大学）；Ontario Veterinary College（安大略兽医学院）；University of Calgary（卡尔加里大学）
Purpose: Thoracic radiographs are commonly used to evaluate patients with confirmed or suspected thoracic pathology. Proper patient positioning is more challenging in canine and feline radiography than in humans due to less patient cooperation and body shape variation. Improper patient positioning during radiograph acquisition has the potential to lead to a misdiagnosis. Asymmetrical hemithoraces are one of the indications of obliquity for which we propose an automatic classification method. Approach: We propose a hemithoraces segmentation method based on Convolutional Neural Networks (CNNs) a…
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）
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…
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）
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）
Problem: There is a lack of big data for the training of deep learning models in medicine, characterized by the time cost of data collection and privacy concerns. Generative adversarial networks (GANs) offer both the potential to generate new data, as well as to use this newly generated data, without inclusion of patients' real data, for downstream applications. Approach: A series of GANs were trained and applied for a downstream computer vision spine radiograph abnormality classification task. Separate classifiers were trained with either access or no access to the original imaging. Trained G…
University of Electronic Science and Technology of China（电子科技大学）
Localization of anatomical landmarks is essential for clinical diagnosis, treatment planning, and research. In this paper, we propose a novel deep network, named feature aggregation and refinement network (FARNet), for the automatic detection of anatomical landmarks. To alleviate the problem of limited training data in the medical domain, our network adopts a deep network pre-trained on natural images as the backbone network and several popular networks have been compared. Our FARNet also includes a multi-scale feature aggregation module for multi-scale feature fusion and a feature refinement …
Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT)（先进磁共振诊断与治疗技术国家工程研究中心）；Med-X Research Institute, Shanghai Jiao Tong University（Med-X研究院，上海交通大学）；Department of Spine Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院脊柱外科部）；Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院放射科）；Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院放射科）
Multi-Centre Validation of a Deep Learning Model for Scoliosis Assessment
Department of Radiogiagnostics, Thomayer University Hospital, Prague, Czechia Department of Radiology；Nuclear Medicine, University Hospital Královské Vinohrady；3rd Faculty of Medicine, Prague, Czechia Research \& Development Department, Carebot, Ltd., Prague, Czechia Department of Simulation Medicine, Masaryk University, 625 00 Brno, Czechia
本研究在十家医院的103张全脊柱X线片上验证了Carebot AI Bones脊柱测量功能，结果显示其Cobb角测量和严重程度分级与专家高度一致，可用于临床报告和分诊。
National University of Sciences and Technology (NUST)（国立科技大学（NUST））；Riphah International University（里法国际大学）
The accurate identification and precise localization of cephalometric landmarks enable the classification and quantification of anatomical abnormalities. The traditional way of marking cephalometric landmarks on lateral cephalograms is a monotonous and time-consuming job. Endeavours to develop automated landmark detection systems have persistently been made, however, they are inadequate for orthodontic applications due to unavailability of a reliable dataset. We proposed a new state-of-the-art dataset to facilitate the development of robust AI solutions for quantitative morphometric analysis. …
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…
AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning
Department of Radiology, Northwestern University, Chicago, IL, USA（西北大学放射科）；Data Science Institute, Vanderbilt University, Nashville, TN, USA（范德比尔特大学数据科学研究院）；Department of Orthopedics, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, P.R. China（山西医科大学第二医院骨科）；Second Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, P.R. China（山西医科大学第二临床医学院）；School of Computer Science, University of Sydney, Sydney, NSW, Australia（悉尼大学计算机科学学院）
SALT: Introducing a Framework for Hierarchical Segmentations in Medical Imaging using Softmax for Arbitrary Label Trees
Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen（埃森大学医院介入与诊断放射学及神经放射学研究所）；Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen（埃森大学医院医学人工智能研究所）；Institute for Transfusion Medicine, University Hospital Essen（埃森大学医院输血医学研究所）；Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen（埃森大学医院医学信息学、生物统计学与流行病学研究所）；Faculty of Computer Science, University of Applied Sciences and Arts（应用科学与艺术大学计算机科学系）
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）
