ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2014
Cortical shell unwrapping for vertebral body abnormality detection on computed tomography.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review.Clinical orthopaedics and related research · 2019Pooled it
- Analysis of factors influencing the intravertebral shell phenomenon after posterior reduction internal fixation of thoracolumbar fracture: a retrospective study.BMC musculoskeletal disorders · 2024Article
- Review
- Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography.Frontiers in endocrinology · 2023Article
- Potential role of artificial intelligence in craniofacial surgery.Archives of craniofacial surgery · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The vertebral body is the main axial load-bearing structure of the spinal vertebra. Assessment of acute injury and chronic deformity of the vertebral body is difficult to assess accurately and quantitatively by simple visual inspection. We propose a cortical shell unwrapping method to examine the vertebral body for injury such as fractures and degenerative osteophytes. The spine is first segmented and partitioned into vertebrae. Then the cortical shell of the vertebral body is extracted using deformable dual-surface models. The cortical shell is then unwrapped onto a 2D map and the complex 3D detection problem is effectively converted to a pattern recognition problem on a 2D plane. Characteristic features adapted for different applications are computed and sent to a committee of support vector machines for classification. The system was evaluated on two applications, one for fracture detection on trauma CT datasets and the other on degenerative osteophyte assessment on sodium fluoride PET/CT. The fracture CAD achieved 93.6% sensitivity at 3.2 false positive per patient and the degenerative osteophyte CAD achieved 82% sensitivity at 4.7 false positive per patient.
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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.