ArticleScientific data2021
A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiologic Insights: Diagnosing Lumbosacral Transitional Vertebrae. Systematic Review of the Literature.Pain practice : the official journal of World Institute of Pain · 2026Pooled it
- Diagnostic accuracy of deep learning vs. human raters for detecting osteoporotic vertebral compression fractures in routine CT scans.European radiology · 2026Article
- Fully Automated Segmentation of Anatomical Subregions of the Thoracolumbar Spine on Computed Tomography.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Spine Neuroimaging: Diagnostic and Prognostic Utility of Novel Biomarkers in Lower Back Pain.Journal of clinical medicine · 2026Review
- Design of 3D-printed hydroxyapatite intervertebral fusion cages in a patient specific framework.Medical & biological engineering & computing · 2026Article
- Article
- A vertebral shape-based classification for labeling the thoracolumbar junction on CT imaging.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 · 2026Article
- Zero-Shot Vertebral Instance Segmentation on DICOM Spine Radiographs Using Promptable Segment Anything Models.Journal of clinical medicine · 2026Article
- Article
- A large, paired dataset of robotic and handheld lumbar spine ultrasound with ground-truth CT benchmarking.Scientific data · 2025Article
- Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset.Scientific data · 2025Article
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images.Radiology. Artificial intelligence · 2025Article
- Enhanced Detection Performance of Acute Vertebral Compression Fractures Using a Hybrid Deep Learning and Traditional Quantitative Measurement Approach: Beyond the Limitations of Genant Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Enhanced nnU-Net Architectures for Automated MRI Segmentation of Head and Neck Tumors in Adaptive Radiation Therapy.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- Improved Detection Accuracy of Chronic Vertebral Compression Fractures by Integrating Height Loss Ratio and Deep Learning Approaches.Diagnostics (Basel, Switzerland) · 2024Article
- An open-access lumbosacral spine MRI dataset with enhanced spinal nerve root structure resolution.Scientific data · 2024Article
- An Automated Vertebrae Localization, Segmentation, and Osteoporotic Compression Fracture Detection Pipeline for Computed Tomographic Imaging.Journal of imaging informatics in medicine · 2024Article
- Lumbar and Thoracic Vertebrae Segmentation in CT Scans Using a 3D Multi-Object Localization and Segmentation CNN.Tomography (Ann Arbor, Mich.) · 2024Article
- Advancing spine care through AI and machine learning: overview and applications.EFORT open reviews · 2024Review
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning segmentation algorithms have been proposed, allowing for subsequent automated analysis of morphology and pathology. The first "Large Scale Vertebrae Segmentation Challenge" (VerSe 2019) showed that these perform well on normal anatomy, but fail in variants not frequently present in the training dataset. Building on that experience, we report on the largely increased VerSe 2020 dataset and results from the second iteration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020 comprises annotated spine computed tomography (CT) images from 300 subjects with 4142 fully visualized and annotated vertebrae, collected across multiple centres from four different scanner manufacturers, enriched with cases that exhibit anatomical variants such as enumeration abnormalities (n = 77) and transitional vertebrae (n = 161). Metadata includes vertebral labelling information, voxel-level segmentation masks obtained with a human-machine hybrid algorithm and anatomical ratings, to enable the development and benchmarking of robust and accurate segmentation algorithms.
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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.