ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2016
A multi-center milestone study of clinical vertebral CT segmentation.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it, 135 citations in OpenAlex.
- Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Fully Automated Segmentation of Anatomical Subregions of the Thoracolumbar Spine on Computed Tomography.Journal of imaging informatics in medicine · 2026Article
- Assessing the reproducibility of a subject-specific finite element modelling pipeline for the human metastatic vertebrae.Scientific reports · 2026Article
- Integrating AI Segmentation, Simulated Digital Twins, and Extended Reality into Medical Education: A Narrative Technical Review and Proof-of-Concept Case Study.Journal of personalized medicine · 2026Review
- Review
- Deep learning model for automated detection of fresh and old vertebral fractures on thoracolumbar CT.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- A patch-based deep learning MRI segmentation model for improving efficiency and clinical examination of the spinal tumor.Journal of bone oncology · 2024Article
- An open annotated dataset and baseline machine learning model for segmentation of vertebrae with metastatic bone lesions from CT.medRxiv : the preprint server for health sciences · 2024Article
- Adjusting for the effect of IV contrast on automated CT body composition measures during the portal venous phase.Abdominal radiology (New York) · 2024Article
- Dataset of Finite Element Models of Normal and Deformed Thoracolumbar Spine.Scientific data · 2024Article
- Lumbar and Thoracic Vertebrae Segmentation in CT Scans Using a 3D Multi-Object Localization and Segmentation CNN.Tomography (Ann Arbor, Mich.) · 2024Article
- Fully automated CT imaging biomarkers for opportunistic prediction of future hip fractures.The British journal of radiology · 2024Article
- Marching Windows: Scalable Mesh Generation for Volumetric Data With Multiple Materials.IEEE transactions on visualization and computer graphics · 2024Article
- The use of deep learning in medical imaging to improve spine care: A scoping review of current literature and clinical applications.North American Spine Society journal · 2023Article
- Addressing Challenges of Opportunistic Computed Tomography Bone Mineral Density Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation.Medical image analysis · 2023Article
- CT image-based biomarkers acquired by AI-based algorithms for the opportunistic prediction of falls.BJR open · 2023Article
- Statistical multi-level shape models for scalable modeling of multi-organ anatomies.Frontiers in bioengineering and biotechnology · 2023Article
- A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Article
Corrections and comments
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Authors and funding
16 authors at 9 institutions in 6 countries.
Funding
Abstract
A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.