The Cobb angle that quantitatively evaluates the spinal curvature plays an important role in the scoliosis diagnosis and treatment. Conventional measurement of these angles suffers from huge variability and low reliability due to intensive manual intervention. However, since there exist high ambiguity and variability around boundaries of vertebrae, it is challenging to obtain Cobb angles automatically. In this paper, we formulate the estimation of the Cobb angles from spinal X-rays as a multi-output regression task. We propose structured support vector regression (S^2VR) to jointly estimate Cobb angles and landmarks of the spine in X-rays in one single framework. The proposed S^2VR can faithfully handle the nonlinear relationship between input images and quantitative outputs, while explicitly capturing the intrinsic correlation of outputs. We introduce the manifold regularization to exploit the geometry of the output space. We propose learning the kernel in S2VR by kernel target alignment to enhance its discriminative ability. The proposed method is evaluated on the spinal X-rays dataset of 439 scoliosis subjects, which achieves the inspiring correlation coefficient of 92.76% with ground truth obtained manually by human experts and outperforms two baseline methods. Our method achieves the direct estimation of Cobb angles with high accuracy, which indicates its great potential in clinical use.
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 …
NepAl Applied Mathematics and Informatics Institute for Research (NAAMII)（尼泊尔应用数学与信息学研究所（NAAMII））；Hospital for Advanced Medical Surgery (HAMS)（高级外科医院（HAMS））
Correct evaluation and treatment of Scoliosis require accurate estimation of spinal curvature. Current gold standard is to manually estimate Cobb Angles in spinal X-ray images which is time consuming and has high inter-rater variability. We propose an automatic method with a novel framework that first detects vertebrae as objects followed by a landmark detector that estimates the 4 landmark corners of each vertebra separately. Cobb Angles are calculated using the slope of each vertebra obtained from the predicted landmarks. For inference on test data, we perform pre and post processings that i…
University of Calcutta（加尔各答大学）；National Institute for the Orthopaedically Handicapped（国家残障矫形研究所）
Cobb angle, which is a measure of spinal curvature is the standard method for quantifying the magnitude of Scoliosis related to spinal deformity in orthopedics. Determining the Cobb angle through manual process is subject to human errors. In this work, we propose a methodology to measure the magnitude of Cobb angle, which appreciably reduces the variability related to its measurement compared to the related works. The proposed methodology is facilitated by using a suitable new improved version of Non-Local Means for image denoisation and Otsus automatic threshold selection for Canny edge detec…
CurvNet: Latent Contour Representation and Iterative Data Engine for Curvature Angle Estimation
School of Computer Science（计算机科学学院）；Technology, China University of Mining（采矿技术，中国矿业大学）；Mine Digitization Engineering Research Center of the Ministry of Education（教育部长矿山数字化工程研究中心）；School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；Xuzhou Central Hospital/The Xuzhou Clinical School of Xuzhou Medical University（徐州中心医院/徐州医学院徐州临床学院）；Xuzhou Rehabilitation Hospital/The Affiliated Xuzhou Rehabilitation Hospital of Xuzhou Medical University（徐州康复医院/徐州医学院附属徐州康复医院）
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…
Automated methods for Cobb angle estimation are of high demand for scoliosis assessment. Existing methods typically calculate the Cobb angle from landmark estimation, or simply combine the low-level task (e.g., landmark detection and spine segmentation) with the Cobb angle regression task, without fully exploring the benefits from each other. In this study, we propose a novel multi-task framework, named Seg4Reg+, which jointly optimizes the segmentation and regression networks. We thoroughly investigate both local and global consistency and knowledge transfer between each other. Specifically, …
ARNAI: Artifact Removal Network based on Autoencoding and Inpainting for Robust Spinal Image Segmentation and Measurement
Pohang Stroke and Spine Hospital（浦项中风与脊柱医院）；Handong Global University（韩东全球大学）；Pohang University of Science and Technology（浦项科技大学）；Kyungpook National University Hospital（庆北国立大学医院）
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 …
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…
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…
Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA（美国新泽西州皮斯卡塔韦市罗格斯大学计算机科学系）
Adolescent idiopathic scoliosis (AIS) is a lifetime disease that arises in children. Accurate estimation of Cobb angles of the scoliosis is essential for clinicians to make diagnosis and treatment decisions. The Cobb angles are measured according to the vertebrae landmarks. Existing regression-based methods for the vertebra landmark detection typically suffer from large dense mapping parameters and inaccurate landmark localization. The segmentation-based methods tend to predict connected or corrupted vertebra masks. In this paper, we propose a novel vertebra-focused landmark detection method. …
Towards contrast-agnostic soft segmentation of the spinal cord
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal（神经Poly实验室，生物医学工程学院，蒙特利尔理工学院）；Mila - Québec Artificial Intelligence Institute（魁北克人工智能研究所）；Translational Imaging in Neurology (ThINK) Basel（神经学转化成像（ThINK）巴塞尔）；Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel（生物医学工程系，医学学院，巴塞尔大学医院和巴塞尔大学）；Department of Neurology, University Hospital Basel（神经病学系，巴塞尔大学医院）；Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel（临床神经免疫学和神经科学巴塞尔研究中心（RC2NB），巴塞尔大学医院和巴塞尔大学）；Neuroradiology Department, Neurocenter of Southern Switzerland, Ospedale Regionale di Lugano（神经放射科，瑞士南部神经中心，卢加诺地区医院）；Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine（康复医学系，科罗拉多大学医学院物理治疗项目）；Division of Pain Medicine, Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine（疼痛医学科，麻醉学、围手术期和疼痛医学部，斯坦福大学医学院）；Functional Neuroimaging Unit, CRIUGM, University of Montreal（功能神经影像单元，CRIUGM，蒙特利尔大学）
Southern Medical University（南方医科大学）；Western University（韦仕敦大学）；Digital Imaging Group of London（伦敦数字影像集团）
Automated quantitative measurement of the spine (i.e., multiple indices estimation of heights, widths, areas, and so on for the vertebral body and disc) is of the utmost importance in clinical spinal disease diagnoses, such as osteoporosis, intervertebral disc degeneration, and lumbar disc herniation, yet still an unprecedented challenge due to the variety of spine structure and the high dimensionality of indices to be estimated. In this paper, we propose a novel cascade amplifier regression network (CARN), which includes the CARN architecture and local shape-constrained manifold regularizatio…
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）
Clinical-Prior Guided Multi-Modal Learning with Latent Attention Pooling for Gait-Based Scoliosis Screening
Orthopaedic Centre, The University of Hong Kong - Shenzhen Hospital, Shenzhen, China（香港大学深圳医院骨科中心）；Translational Medicine Centre, The University of Hong Kong - Shenzhen Hospital, China（香港大学深圳医院转化医学中心）；Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China（香港大学李嘉成医学院骨科及创伤外科学院）；Huawei, China（华为）
Huazhong University of Science and Technology（华中科技大学）；Wuhan National Laboratory for Optoelectronics（武汉光电国家研究中心）；City University of Hong Kong（香港城市大学）；The Hong Kong University of Science and Technology（香港科技大学）；Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属协和医院）
Vertebral landmark localization is a crucial step for variant spine-related clinical applications, which requires detecting the corner points of 17 vertebrae. However, the neighbor landmarks often disturb each other for the homogeneous appearance of vertebrae, which makes vertebral landmark localization extremely difficult. In this paper, we propose multi-stage cascaded convolutional neural networks (CNNs) to split the single task into two sequential steps, i.e., center point localization to roughly locate 17 center points of vertebrae, and corner point localization to find 4 corner points for…
Accurate identification and localization of the vertebrae in CT scans is a critical and standard preprocessing step for clinical spinal diagnosis and treatment. Existing methods are mainly based on the integration of multiple neural networks, and most of them use the Gaussian heat map to locate the vertebrae's centroid. However, the process of obtaining the vertebrae's centroid coordinates using heat maps is non-differentiable, so it is impossible to train the network to label the vertebrae directly. Therefore, for end-to-end differential training of vertebra coordinates on CT scans, a robust …
Symmetric Perception and Ordinal Regression for Detecting Scoliosis Natural Image
Xuzhou Central Hospital（徐州市中心医院）；The Xuzhou Clinical School of Xuzhou Medical University（徐州医科大学徐州临床学院）；Xuzhou Rehabilitation Hospital（徐州市康复医院）；The Affiliated Xuzhou Rehabilitation Hospital of Xuzhou Medical University（徐州医科大学附属徐州康复医院）；School of Computer Science and Technology, China University of Mining and Technology（中国矿业大学计算机科学与技术学院）；The First Affiliated Hospital, Sun Yat-Sen University（中山大学附属第一医院）；The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学附属第一医院（中国科学技术大学生命科学与医学部））
