# SpineFM: Leveraging Foundation Models for Automatic Spine X-ray Segmentation

arXiv: https://arxiv.org/abs/2411.00326 · HTML: https://mlsanity.com/paper/2411.00326/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 1 Nov 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [The Ladder Algorithm: Finding Repetitive Structures in Medical Images by Induction](https://mlsanity.com/paper/2001.11284/) (2020)
- [Spinal Osteophyte Detection via Robust Patch Extraction on minimally annotated X-rays](https://mlsanity.com/paper/2402.19263/) (2024)

## Built on by (most cited first, 1 of 1)

- [Emerging Semantic Segmentation from Positive and Negative Coarse Label Learning](https://mlsanity.com/paper/2508.18186/) (2025)

## Abstract (arXiv)

This paper introduces SpineFM, a novel pipeline that achieves state-of-the-art performance in the automatic segmentation and identification of vertebral bodies in cervical and lumbar spine radiographs. SpineFM leverages the regular geometry of the spine, employing a novel inductive process to sequentially infer the location of each vertebra along the spinal column. Vertebrae are segmented using Medical-SAM-Adaptor, a robust foundation model that diverges from commonly used CNN-based models. We achieved outstanding results on two publicly available spine X-Ray datasets, with successful identification of 97.8% and 99.6% of annotated vertebrae, respectively. Of which, our segmentation reached an average Dice of 0.942 and 0.921, surpassing previous state-of-the-art methods.