# Lumbar spine segmentation in MR images: a dataset and a public benchmark

arXiv: https://arxiv.org/abs/2306.12217 · HTML: https://mlsanity.com/paper/2306.12217/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 21 Jun 2023.

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

- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography](https://mlsanity.com/paper/2105.14711/) (2021)
- [Iterative fully convolutional neural networks for automatic vertebra segmentation and identification](https://mlsanity.com/paper/1804.04383/) (2018)
- [Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks](https://mlsanity.com/paper/2009.13798/) (2020)
- [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, 30 of 30)

- [FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models](https://mlsanity.com/paper/2407.00983/) (2024)
- [How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model](https://mlsanity.com/paper/2404.09957/) (2024)
- [MRI-CORE: A Foundation Model for Magnetic Resonance Imaging](https://mlsanity.com/paper/2506.12186/) (2025)
- [SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI](https://mlsanity.com/paper/2401.12974/) (2024)
- [Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder](https://mlsanity.com/paper/2408.01571/) (2024)
- [SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation](https://mlsanity.com/paper/2402.16368/) (2024)
- [HyperSpace: Hypernetworks for spacing-adaptive image segmentation](https://mlsanity.com/paper/2407.03681/) (2024)
- [MedicoSAM: Robust Improvement of SAM for Medical Imaging](https://mlsanity.com/paper/2501.11734/) (2025)
- [Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge](https://mlsanity.com/paper/2504.02382/) (2025)
- [Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications](https://mlsanity.com/paper/2508.07165/) (2025)
- [Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language](https://mlsanity.com/paper/2603.02790/) (2026)
- [Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action](https://mlsanity.com/paper/2606.15647/) (2026)
- [Panoptic Segmentation and Labelling of Lumbar Spine Vertebrae using Modified Attention Unet](https://mlsanity.com/paper/2404.18291/) (2024)
- [Anatomy-Aware Text-Visual Fusion with Dual-Perspective Prompts for Fine-Grained Lumbar Spine Segmentation](https://mlsanity.com/paper/2504.03476/) (2025)
- [SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus](https://mlsanity.com/paper/2510.03160/) (2025)
- [PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis](https://mlsanity.com/paper/2607.19696/) (2026)
- [Pioneering Precision in Lumbar Spine MRI Segmentation with Advanced Deep Learning and Data Enhancement](https://mlsanity.com/paper/2409.06018/) (2024)
- [Do Superpixel Segmentation Methods Influence Deforestation Image Classification?](https://mlsanity.com/paper/2510.04645/) (2025)
- [SpineBench: Benchmarking Multimodal LLMs for Spinal Pathology Analysis](https://mlsanity.com/paper/2510.12267/) (2025)
- [Geometrical Cross-Attention and Nonvoid Voxelization for Efficient 3D Medical Image Segmentation](https://mlsanity.com/paper/2604.05515/) (2026)
- [AI Approach for MRI-only Full-Spine Vertebral Segmentation and 3D Reconstruction in Paediatric Scoliosis](https://mlsanity.com/paper/2604.17846/) (2026)
- [Virtual-reality based patient-specific simulation of spine surgical procedures: A fast, highly automated and high-fidelity system for surgical education and planning](https://mlsanity.com/paper/2604.26781/) (2026)
- [One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation](https://mlsanity.com/paper/2605.03098/) (2026)
- [Vision Transformer-Conditioned UNet for Domain-Adaptive Semantic Segmentation](https://mlsanity.com/paper/2605.16393/) (2026)
- [SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI](https://mlsanity.com/paper/2606.10021/) (2026)
- [Towards Voxel Spacing Consistency for Medical Image Segmentation](https://mlsanity.com/paper/2606.31839/) (2026)
- [SLIP: Segmentation with Low-latency Interactive Prompting for 3D Medical Images](https://mlsanity.com/paper/2607.22332/) (2026)
- [Beyond Fluency: A Clinical Benchmark and Anomaly-Enhanced Baseline for Spine MRI Report Generation](https://mlsanity.com/paper/2608.07117/) (2026)
- [CoM3eT: A foundation model for medical image analysis through federated, multidimensional context integration](https://mlsanity.com/paper/2608.16268/) (2026)
- [GateSPINE: Gated Cross-View Fusion for Lumbar Spine MRI Report Generation](https://mlsanity.com/paper/2609.40091/) (2026)

## Abstract (arXiv)

This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain and was collected from four different hospitals. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. Performance values were computed on a sequestered set of 39 studies with 97 series, which were additionally used to set up a continuous segmentation challenge that allows for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation and improve the diagnostic value of lumbar spine MRI.