# A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data

arXiv: https://arxiv.org/abs/2103.06360 · HTML: https://mlsanity.com/paper/2103.06360/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [Iterative fully convolutional neural networks for automatic vertebra segmentation and identification](https://mlsanity.com/paper/1804.04383/) (2018)
- [Fully Automatic Segmentation of Lumbar Vertebrae from CT Images using Cascaded 3D Fully Convolutional Networks](https://mlsanity.com/paper/1712.01509/) (2017)
- [Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior](https://mlsanity.com/paper/1804.01307/) (2018)
- [A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets](https://mlsanity.com/paper/1703.04347/) (2017)

## Built on by (most cited first, 28 of 28)

- [M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models](https://mlsanity.com/paper/2404.00578/) (2024)
- [Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset](https://mlsanity.com/paper/2406.06512/) (2024)
- [STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training](https://mlsanity.com/paper/2304.06716/) (2023)
- [SegVol: Universal and Interactive Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2311.13385/) (2023)
- [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025)
- [Segment Anything Model for Medical Images?](https://mlsanity.com/paper/2304.14660/) (2023)
- [MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision](https://mlsanity.com/paper/2308.16139/) (2023)
- [Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding](https://mlsanity.com/paper/2210.03416/) (2022)
- [3DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representation](https://mlsanity.com/paper/2312.15676/) (2023)
- [LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging](https://mlsanity.com/paper/2502.20985/) (2025)
- [Learning with Explicit Shape Priors for Medical Image Segmentation](https://mlsanity.com/paper/2303.17967/) (2023)
- [Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder](https://mlsanity.com/paper/2408.01571/) (2024)
- [Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive Networks](https://mlsanity.com/paper/2312.07630/) (2023)
- [Comp2Comp: Open-Source Body Composition Assessment on Computed Tomography](https://mlsanity.com/paper/2302.06568/) (2023)
- [Shape Completion in the Dark: Completing Vertebrae Morphology from 3D Ultrasound](https://mlsanity.com/paper/2404.07668/) (2024)
- [Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading](https://mlsanity.com/paper/2303.12031/) (2023)
- [The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection](https://mlsanity.com/paper/2509.15947/) (2025)
- [VertDetect: Fully End-to-End 3D Vertebral Instance Segmentation Model](https://mlsanity.com/paper/2311.09958/) (2023)
- [Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding](https://mlsanity.com/paper/2312.05220/) (2023)
- [SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus](https://mlsanity.com/paper/2510.03160/) (2025)
- [Automatic laminectomy cutting plane planning based on artificial intelligence in robot assisted laminectomy surgery](https://mlsanity.com/paper/2312.17266/) (2023)
- [EAR: Edge-Aware Reconstruction of 3-D vertebrae structures from bi-planar X-ray images](https://mlsanity.com/paper/2407.20937/) (2024)
- [Reconstruction of 3D lumbar spine models from incomplete segmentations using landmark detection](https://mlsanity.com/paper/2412.05065/) (2024)
- [M^3Builder: A Multi-Agent System for Automated Machine Learning in Medical Imaging](https://mlsanity.com/paper/2502.20301/) (2025)
- [Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation](https://mlsanity.com/paper/2504.06741/) (2025)
- [SpineBench: Benchmarking Multimodal LLMs for Spinal Pathology Analysis](https://mlsanity.com/paper/2510.12267/) (2025)
- [SpineContextResUNet: A Computationally Efficient Residual UNet for Spine CT Segmentation](https://mlsanity.com/paper/2605.20760/) (2026)
- [CTSpinoPelvic1K: spine, pelvis, ribs and femora in one coordinate frame, annotated for lumbosacral transitional anatomy](https://mlsanity.com/paper/2609.22760/) (2026)

## Abstract (arXiv)

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.