ArticlePloS one2015
Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 126 citations in OpenAlex.
- Statistical shape modelling of the human lumbar spine: a scoping review characterising the research landscape and potential clinical applications.Frontiers in bioengineering and biotechnology · 2026Pooled it
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Medical Spine Sagittal MRI Dataset for Segmentation and Foraminal Stenosis detection.Scientific data · 2026Article
- Lumbar and pelvic CT image segmentation based on cross-scale feature fusion and linear self-attention mechanism.Scientific reports · 2025Article
- An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging.International journal of computer assisted radiology and surgery · 2024Article
- Lumbar and Thoracic Vertebrae Segmentation in CT Scans Using a 3D Multi-Object Localization and Segmentation CNN.Tomography (Ann Arbor, Mich.) · 2024Article
- Lumbar spine segmentation in MR images: a dataset and a public benchmark.Scientific data · 2024Article
- Development of a shape-based algorithm for identification of asymptomatic vertebral compression fractures: A proof-of-principle study.Osteoporosis and sarcopenia · 2024Article
- End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images.Computational intelligence and neuroscience · 2023Article
- Current development and prospects of deep learning in spine image analysis: a literature review.Quantitative imaging in medicine and surgery · 2022Review
- Toward an artificial intelligence-assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response.International journal for numerical methods in biomedical engineering · 2022Article
- Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy.Scientific reports · 2022Article
- Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database.Scientific data · 2022Article
- Verte-Box: A Novel Convolutional Neural Network for Fully Automatic Segmentation of Vertebrae in CT Image.Tomography (Ann Arbor, Mich.) · 2022Article
- Clinical utility of convolutional neural networks for treatment planning in radiotherapy for spinal metastases.Physics and imaging in radiation oncology · 2022Article
- The lumbar region localization using bone anatomy feature graphs.Medical & biological engineering & computing · 2021Article
- Automated Pipeline to Generate Anatomically Accurate Patient-Specific Biomechanical Models of Healthy and Pathological FSUs.Frontiers in bioengineering and biotechnology · 2021Article
- Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision.Global spine journal · 2020Article
- Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening.European radiology · 2020Article
- Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data.La Radiologia medica · 2020Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.
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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.