发表机构
ETH Zürich; Swiss Institute of Bioinformatics (SIB)（苏黎世联邦理工学院; 瑞士生物信息学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出SpineSegDiff扩散框架，基于SPIDER数据集实现腰椎MRI的语义分割，性能接近nnUnet，可提升退化椎间盘识别，不确定性图助力临床审查，有望增强下腰痛的诊断与管理。
AI 中文摘要
本研究提出一种基于扩散的框架，用于对下腰痛（LBP）患者的腰椎MRI扫描进行鲁棒且准确的语义分割，无论扫描是T1加权还是T2加权。我们使用SPIDER数据集，与用于分割椎体、椎间盘（IVDs）和椎管的先进模型进行对比。结果显示，SpineSegDiff的分割性能可与最先进的非扩散模型nnUnet相媲美，尤其在退化椎间盘的识别方面有所提升。此外，我们的模型生成的不确定性图为临床审查提供了有价值的参考，增强了分割结果的鲁棒性和可靠性。本研究的发现强调了扩散模型通过更精准分析病理脊柱MRI，在增强下腰痛的诊断和管理方面的潜力。
英文摘要
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.
CommentsMaria Monzon and Thomas Iff contributed equally to this work. Published in Proceedings of The 8th International Conference on Medical Imaging with Deep Learning (MIDL 2025), PMLR volume 301, pages 1145-1163, 2026
Journal refProceedings of Machine Learning Research 301:1145-1163, 2026
