Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentation

Maria Monzon, Thomas Iff, Ender Konukoglu, Catherine R Jutzeler

Keywords: Diffusion Models, Lumbar MRI, presegmentation

TL;DR: A diffusion-based model, achieves state-of-the-art performance in pathological IVD disc semantic segmentation of lumbar spine MRI scans for low back pain patients, providing valuable variability maps for clinical decision-making

Abstract:

This study introduces a diffusion-based framework for robust and accurate semantic segmentation of lumbar spine MRI scans from patients with low back pain (LBP), regardless of whether the scans are T1- or T2-weighted.
We compared with advanced models for segmenting vertebrae, intervertebral discs (IVDs), and spinal canal using the SPIDER dataset.
The results showed that SpineSegDiff achieved a segmentation performance comparable to that of the state-of-the-art non-diffusion nnUnet, particularly in improving the identification of degenerated IVDs.
In addition, the uncertainty maps generated by our model provide valuable insights for clinical review, enhancing the robustness and reliability of the segmentation results.
The potential of diffusion models to enhance the diagnosis and management of LBP through more precise analysis of pathological spine MRI is underscored by our findings.

Primary Subject Area: Segmentation

Secondary Subject Area: Generative Models

Paper Type: Validation or Application

Registration Requirement: Yes

Reproducibility: https://gitlab.ethz.ch/BMDSlab/publications/low-back/diffusion-models-for-lumbar-spine-mri-segmentation

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Submission Number: 156