ArticleScientific reports2021
Automatic vertebrae localization and segmentation in CT with a two-stage Dense-U-Net.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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29 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.
- Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation.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 · 2022Pooled it
- Fully Automated Segmentation of Anatomical Subregions of the Thoracolumbar Spine on Computed Tomography.Journal of imaging informatics in medicine · 2026Article
- Enhanced vertebrae localization in CT volumes: a two-stage deep learning framework.BMC medical imaging · 2026Article
- Segmentation and 3D Visualization of Spinal Motion Segments from MSCT Images Using a 3D U‑Net Framework.Journal of imaging informatics in medicine · 2026Article
- Deep Learning-Based Identification of Surgical Candidacy for Cervical Spinal Cord Decompression.International journal of spine surgery · 2026Article
- Boundary sensitive-net-based lumbar vertebra segmentation and spondylolisthesis measurement.Scientific reports · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Structure-aware multi-task learning with domain generalization for robust vertebrae analysis in spinal CT.NPJ digital medicine · 2026Article
- Machine learning and quantitative computed tomography radiomics prediction of postoperative functional recovery in paraplegic dogs.Veterinary surgery : VS · 2025Article
- High-Fidelity Finite Element Modeling Technique to Improve Sensitivity to Bone Tissue Changes of Older Adults with Obesity undergoing Intensive Lifestyle Intervention.Annals of biomedical engineering · 2025Article
- 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
- Automatic pelvic fracture segmentation: a deep learning approach and benchmark dataset.Frontiers in medicine · 2025Article
- Deep Learning for Discrimination of Early Spinal Tuberculosis from Acute Osteoporotic Vertebral Fracture on CT.Infection and drug resistance · 2025Article
- Explainable fully automated CT scoring of interstitial lung disease for patients suspected of systemic sclerosis by cascaded regression neural networks and its comparison with experts.Scientific reports · 2024Article
- Development and reporting of artificial intelligence in osteoporosis management.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- VerFormer: Vertebrae-Aware Transformer for Automatic Spine Segmentation from CT Images.Diagnostics (Basel, Switzerland) · 2024Article
- Automated detection, labelling and radiological grading of clinical spinal MRIs.Scientific reports · 2024Article
- Measurement of distances and locations of thoracic and lumbar vertebral bodies from CT scans in cases of spinal deformation.BMC medical imaging · 2024Article
- Lumbar and Thoracic Vertebrae Segmentation in CT Scans Using a 3D Multi-Object Localization and Segmentation CNN.Tomography (Ann Arbor, Mich.) · 2024Article
- Using Augmented Reality Technology to Optimize Transfacet Lumbar Interbody Fusion: A Case Report.Journal of clinical medicine · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic vertebrae localization and segmentation in computed tomography (CT) are fundamental for spinal image analysis and spine surgery with computer-assisted surgery systems. But they remain challenging due to high variation in spinal anatomy among patients. In this paper, we proposed a deep-learning approach for automatic CT vertebrae localization and segmentation with a two-stage Dense-U-Net. The first stage used a 2D-Dense-U-Net to localize vertebrae by detecting the vertebrae centroids with dense labels and 2D slices. The second stage segmented the specific vertebra within a region-of-interest identified based on the centroid using 3D-Dense-U-Net. Finally, each segmented vertebra was merged into a complete spine and resampled to original resolution. We evaluated our method on the dataset from the CSI 2014 Workshop with 6 metrics: location error (1.69 ± 0.78 mm), detection rate (100%) for vertebrae localization; the dice coefficient (0.953 ± 0.014), intersection over union (0.911 ± 0.025), Hausdorff distance (4.013 ± 2.128 mm), pixel accuracy (0.998 ± 0.001) for vertebrae segmentation. The experimental results demonstrated the efficiency of the proposed method. Furthermore, evaluation on the dataset from the xVertSeg challenge with location error (4.12 ± 2.31), detection rate (100%), dice coefficient (0.877 ± 0.035) shows the generalizability of our method. In summary, our solution localized the vertebrae successfully by detecting the centroids of vertebrae and implemented instance segmentation of vertebrae in the whole spine.
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