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

Maria Monzon, Thomas Iff, Ender Konukoglu, Catherine R Jutzeler
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:1145-1163, 2026.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-monzon26a, title = {Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentation}, author = {Monzon, Maria and Iff, Thomas and Konukoglu, Ender and Jutzeler, Catherine R}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {1145--1163}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/monzon26a/monzon26a.pdf}, url = {https://proceedings.mlr.press/v301/monzon26a.html}, 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.} }
Endnote
%0 Conference Paper %T Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentation %A Maria Monzon %A Thomas Iff %A Ender Konukoglu %A Catherine R Jutzeler %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-monzon26a %I PMLR %P 1145--1163 %U https://proceedings.mlr.press/v301/monzon26a.html %V 301 %X 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.
APA
Monzon, M., Iff, T., Konukoglu, E. & Jutzeler, C.R.. (2026). Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentation. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:1145-1163 Available from https://proceedings.mlr.press/v301/monzon26a.html.

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