English

Diffusion-Based Semantic Segmentation of Lumbar Spine MRI Scans of Lower Back Pain Patients

Image and Video Processing 2024-11-20 v2 Computer Vision and Pattern Recognition Machine Learning

Abstract

This study introduces a diffusion-based framework for robust and accurate segmenton of vertebrae, intervertebral discs (IVDs), and spinal canal from Magnetic Resonance Imaging~(MRI) scans of patients with low back pain (LBP), regardless of whether the scans are T1w or T2-weighted. The results showed that SpineSegDiff achieved comparable outperformed non-diffusion state-of-the-art models in the identification of degenerated IVDs. Our findings highlight the potential of diffusion models to improve LBP diagnosis and management through precise spine MRI analysis.

Keywords

Cite

@article{arxiv.2411.10755,
  title  = {Diffusion-Based Semantic Segmentation of Lumbar Spine MRI Scans of Lower Back Pain Patients},
  author = {Maria Monzon and Thomas Iff and Ender Konukoglu and Catherine R. Jutzeler},
  journal= {arXiv preprint arXiv:2411.10755},
  year   = {2024}
}

Comments

Findings paper presented at Machine Learning for Health (ML4H) symposium 2024, December 15-16, 2024, Vancouver, Canada, 5 pages