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 infer vertebrae labels (by locating the 'reference' vertebrae which are easier to segment than others) for a given spinal X-ray image. We evaluated the proposed pipeline on three spinal X-ray datasets (two internal and one publicly available), and compared against vertebrae annotated by radiologists. Our experimental results have shown that the proposed pipeline outperformed two SOTA segmentation models on our test dataset (MEASURE 1) with a mean Dice of 0.90, vs. a mean Dice of 0.73 for Mask R-CNN and 0.72 for U-Net. To further evaluate the generalization ability of VertXNet, the pre-trained pipeline was directly tested on two additional datasets (PREVENT and NHANES II) and consistent performance was observed with a mean Dice of 0.89 and 0.88, respectively. Overall, VertXNet demonstrated significantly improved performance for vertebra segmentation and labeling for spinal X-ray imaging, and evaluation on both in-house clinical trial data and publicly available data further proved its generalization.
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…
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…
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）
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）
Spinal-Multiple-Myeloma-SEG: A Dual-Energy CT Dataset Extended with Trabecular Bone Segmentation of Lumbar Vertebrae
Brno University of Technology（布尔诺理工大学）；University Hospital & Faculty of Medicine, Ostrava（俄斯特拉发大学医院与医学院）；University Hospital Brno（布尔诺大学医院）；Faculty of Medicine, Masaryk University（马萨里克大学医学院）；Masaryk University（马萨里克大学）
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…
University of Oxford（牛津大学）；Plexalis Ltd（Plexalis有限公司）
This paper proposes a novel transformer-based model architecture for medical imaging problems involving analysis of vertebrae. It considers two applications of such models in MR images: (a) detection of spinal metastases and the related conditions of vertebral fractures and metastatic cord compression, (b) radiological grading of common degenerative changes in intervertebral discs. Our contributions are as follows: (i) We propose a Spinal Context Transformer (SCT), a deep-learning architecture suited for the analysis of repeated anatomical structures in medical imaging such as vertebral bodies…
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）
SurgPointTransformer: Vertebrae Shape Completion with RGB-D Data
Research in Orthopedic Computer Science, University Hospital Balgrist, University of Zürich（苏黎世大学、巴尔格里斯特大学医院骨科计算机科学研究所）；Balgrist University Hospital, University of Zurich（苏黎世大学巴尔格里斯特大学医院）
HealthiVert-GAN: A Novel Framework of Pseudo-Healthy Vertebral Image Synthesis for Interpretable Compression Fracture Grading
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（医-X研究院）；Kashi Prefecture Second People’s Hospital（喀什县第二人民医院）；Renji Hospital, Shanghai, China（仁济医院）；Shanghai Sixth People’s Hospital, Shanghai, China（上海第六人民医院）
Z 2 Sky Technologies Inc.（Z 2 天工科技公司）；University of Rochester（罗切斯特大学）；Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS（中国科学院计算技术研究所 中国科学院智能信息处理重点实验室）；University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advance Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）
Spinal surgery planning necessitates automatic segmentation of vertebrae in cone-beam computed tomography (CBCT), an intraoperative imaging modality that is widely used in intervention. However, CBCT images are of low-quality and artifact-laden due to noise, poor tissue contrast, and the presence of metallic objects, causing vertebra segmentation, even manually, a demanding task. In contrast, there exists a wealth of artifact-free, high quality CT images with vertebra annotations. This motivates us to build a CBCT vertebra segmentation model using unpaired CT images with annotations. To overco…
