# A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI

arXiv: https://arxiv.org/abs/2007.02606 · HTML: https://mlsanity.com/paper/2007.02606/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling](https://mlsanity.com/paper/2108.06554/) (2021)
- [Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization](https://mlsanity.com/paper/2204.06818/) (2022)
- [Context-Aware Transformers For Spinal Cancer Detection and Radiological Grading](https://mlsanity.com/paper/2206.13173/) (2022)
- [SpineNetV2: Automated Detection, Labelling and Radiological Grading Of Clinical MR Scans](https://mlsanity.com/paper/2205.01683/) (2022)
- [Self-Supervised Multi-Modal Alignment for Whole Body Medical Imaging](https://mlsanity.com/paper/2107.06652/) (2021)
- [X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models](https://mlsanity.com/paper/2404.19604/) (2024)
- [UKBOB: One Billion MRI Labeled Masks for Generalizable 3D Medical Image Segmentation](https://mlsanity.com/paper/2504.06908/) (2025)
- [AI-Driven MRI Spine Pathology Detection: A Comprehensive Deep Learning Approach for Automated Diagnosis in Diverse Clinical Settings](https://mlsanity.com/paper/2503.20316/) (2025)
- [VertXNet: An Ensemble Method for Vertebrae Segmentation and Identification of Spinal X-Ray](https://mlsanity.com/paper/2302.03476/) (2023)
- [Contouring by Unit Vector Field Regression](https://mlsanity.com/paper/2305.17024/) (2023)
- [3D Spine Shape Estimation from Single 2D DXA](https://mlsanity.com/paper/2412.01504/) (2024)
- [Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentation](https://mlsanity.com/paper/2608.04906/) (2026)

## Abstract (arXiv)

We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs. This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies and convolutional image-to-image translation followed by beam search to label vertebral levels in a self-consistent manner. The method can be applied without modification to lumbar, cervical and thoracic-only scans across a range of different MR sequences. The resulting system achieves 98.1% detection rate and 96.5% identification rate on a challenging clinical dataset of whole spine scans and matches or exceeds the performance of previous systems on lumbar-only scans. Finally, we demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.