Diffusion-Based Semantic Segmentation of Lumbar Spine MRI Scans of Lower Back Pain Patients
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916487724269568 |
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| author | Monzon, Maria Iff, Thomas Konukoglu, Ender Jutzeler, Catherine R. |
| author_facet | Monzon, Maria Iff, Thomas Konukoglu, Ender Jutzeler, Catherine R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10755 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diffusion-Based Semantic Segmentation of Lumbar Spine MRI Scans of Lower Back Pain Patients Monzon, Maria Iff, Thomas Konukoglu, Ender Jutzeler, Catherine R. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Diffusion-Based Semantic Segmentation of Lumbar Spine MRI Scans of Lower Back Pain Patients |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.10755 |