mlsanityData to 14 Oct 2026

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

arXiv 2608.04906 ↗First posted 5 Aug 2026cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 21 of its 39 references that are in the map. First posted on arXiv 5 Aug 2026.

Builds on

21 of 39 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation18 Mar
  2. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey14 Nov
  3. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model1 Nov
  4. 2022Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation27 Oct
  5. 2021Diffusion Models for Implicit Image Segmentation Ensembles6 Dec

Other references · 16

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
  6. 2020VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Imagescited by 111
  7. 2019Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practicecited by 71
  8. 2023On the Generalization Properties of Diffusion Modelscited by 68
  9. 2022MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusioncited by 67
  10. 2022Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentationcited by 55
  11. 2018A Gentle Introduction to Deep Learning in Medical Image Processingcited by 34
  12. 2018Iterative fully convolutional neural networks for automatic vertebra segmentation and identificationcited by 25
4 more
  1. 2023From CNN to Transformer: A Review of Medical Image Segmentation Modelscited by 22
  2. 2020A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRIcited by 12
  3. 2018DeepSPINE: Automated Lumbar Vertebral Segmentation, Disc-level Designation, and Spinal Stenosis Grading Using Deep Learningcited by 5
  4. 2021SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebraecited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Diffusion-Based Semantic Segmentation of Lumbar Spine MRI Scans of Lower Back Pain Patients
  2. 2023Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation
  3. 2024ContourDiff: Unpaired Medical Image Translation with Structural Consistency
  4. 2023Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation
  5. 2024SpineMamba: Enhancing 3D Spinal Segmentation in Clinical Imaging through Residual Visual Mamba Layers and Shape Priors
  6. 2024Energy-Based Prior Latent Space Diffusion model for Reconstruction of Lumbar Vertebrae from Thick Slice MRI
  7. 2026An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis
  8. 2024Latent Diffusion for Medical Image Segmentation: End to end learning for fast sampling and accuracy