Accurate vertebra localization and identification are required in many clinical applications of spine disorder diagnosis and surgery planning. However, significant challenges are posed in this task by highly varying pathologies (such as vertebral compression fracture, scoliosis, and vertebral fixation) and imaging conditions (such as limited field of view and metal streak artifacts). This paper proposes a robust and accurate method that effectively exploits the anatomical knowledge of the spine to facilitate vertebra localization and identification. A key point localization model is trained to produce activation maps of vertebra centers. They are then re-sampled along the spine centerline to produce spine-rectified activation maps, which are further aggregated into 1-D activation signals. Following this, an anatomically-constrained optimization module is introduced to jointly search for the optimal vertebra centers under a soft constraint that regulates the distance between vertebrae and a hard constraint on the consecutive vertebra indices. When being evaluated on a major public benchmark of 302 highly pathological CT images, the proposed method reports the state of the art identification (id.) rate of 97.4%, and outperforms the best competing method of 94.7% id. rate by reducing the relative id. error rate by half.
SpineCLUE: Automatic Vertebrae Identification Using Contrastive Learning and Uncertainty Estimation
Southeast University（东南大学）；Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing（江苏省医学信息处理国际联合研究实验室）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education（教育部新一代人工智能技术及其交叉应用重点实验室（东南大学））
Spinal ligaments detection on vertebrae meshes using registration and 3D edge detection
University of Koblenz（科布伦茨大学）；Institute for Computational Visualistics（计算视觉研究所）；Intelligent Autonomous Systems Group（智能自主系统组）；Institute for Medical Technology and Information Processing（医学技术与信息处理研究所）
VerteNet -- A Multi-Context Hybrid CNN Transformer for Accurate Vertebral Landmark Localization in Lateral Spine DXA Images
Centre for AI \& ML, School of Science, Edith Cowan University, Australia Nutrition \& Health Innovation Research Institute, Edith Cowan University, Australia School of Science, Sun Yat-sen University, Guangzhou, China School of Medical；Health Sciences, Edith Cowan University, Joondalup, Western Australia Park Nicollet Clinic；HealthPartners Institute, Minnesota, USA Department of Medicine；Radiology, University of Manitoba, Manitoba, Canada Department of Health Sciences, McMaster University, Hamilton, Canada Computer Science；Software Engineering, The University of Western Australia, Perth, Western Australia
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）
Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs
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）
Tracing 3D Anatomy in 2D Strokes: A Multi-Stage Projection Driven Approach to Cervical Spine Fracture Identification
Department of Computer Science and Engineering, BRAC University（布拉格大学计算机科学与工程系）；Department of Electrical and Electronic Engineering, University of Dhaka（达卡大学电子与电气工程系）；Department of Electrical Engineering, Qatar University（卡塔尔大学电子工程系）；Department of Radiology, Hamad Medical Corporation（哈马德医疗中心放射科）
Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine
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）
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）
Registration of Longitudinal Spine CTs for Monitoring Lesion Growth
Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Brigham and Women’s Hospital（布莱根妇女医院）；Isomics Inc（伊索米克斯公司）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）
Automatic Spine Segmentation using Convolutional Neural Network via Redundant Generation of Class Labels for 3D Spine Modeling
Korea Institute of Science and Technology（韩国科学技术研究院）；Korea University of Science and Technology（韩国科学技术大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）
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）
Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification
Center for Data Science, Peking University（北京大学数据科学中心）；National Engineering Laboratory for Big Data Analysis and Applications, Peking University（北京大学大数据分析与应用国家工程实验室）；National Biomedical Imaging Center, Peking University（北京大学生物医学成像国家中心）
An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCT
Department of Computer Science, Kiel University（基尔大学计算机科学系）；Section Biomedical Imaging, Molecular Imaging North Competence Center (MOIN CC), Department of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein (UKSH), Kiel University（基尔大学石勒苏益格-荷尔斯泰因大学医学中心（UKSH）放射科和神经放射科分子成像北中心（MOIN CC）生物医学成像部）；Institute of Diagnostic and Interventional Radiology/Neuroradiology, Westküstenklinikum Heide, Academic Teaching Hospital of the Universities of Kiel, Lübeck and Hamburg（基尔、吕贝克和汉堡大学的学术教学医院Heide西海岸医院诊断与介入放射学/神经放射学研究所）
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）
Virtual-reality based patient-specific simulation of spine surgical procedures: A fast, highly automated and high-fidelity system for surgical education and planning
Holland Bone and Joint Program, Sunnybrook Research Institute（霍尔德骨科与关节程序，圣布鲁诺研究学院）；Division of Spine Surgery, Sunnybrook Health Sciences Centre（脊柱外科部，圣布鲁诺健康科学中心）；Division of Orthopaedic Surgery, Department of Surgery, University of Toronto（骨科部，外科部，多伦多大学）
Automatic Semantic Segmentation of the Lumbar Spine: Clinical Applicability in a Multi-parametric and Multi-centre Study on Magnetic Resonance Images
Fundaciò per al Foment de la Investigaciò Sanitària i Biomèdica (FISABIO)（瓦伦西亚卫生与生物医学研究促进基金会（FISABIO））；Hospital Arnau de Vilanova（阿尔瑙·德·比拉诺瓦医院）；Universitat Politècnica de València（瓦伦西亚理工大学）；Regional ministry of Universal Health and Public Health in Valencia（瓦伦西亚全民健康与公共卫生部）
