用扩散模型提升腰痛患者腰椎MRI分割精度，尤其改善退变椎间盘识别。
Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentation
- 基于扩散模型的分割框架，适配不同序列的腰椎MRI数据。
- 在SPIDER数据集上性能接近nnUnet，退变椎间盘分割更准确。
- 生成不确定度图，辅助临床判读，适合医学影像分析研究者。
本研究提出一种基于扩散模型的腰椎脊柱MRI语义分割框架，适用于低背痛（LBP）患者的T1和T2加权扫描。在SPIDER数据集上，与先进模型对比，SpineSegDiff在椎体、椎间盘（IVDs）和脊髓腔分割中表现优异，尤其在识别退变椎间盘方面显著优于现有方法。此外，模型生成的不确定性图可为临床评估提供重要参考，增强分割结果的鲁棒性与可靠性。研究结果表明，扩散模型能通过更精准的病理脊柱MRI分析，助力低背痛的诊断与管理。
原文摘要 · Abstract (English)
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.
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