This work examines a disc-centric approach for automated severity grading of lumbar spinal stenosis from sagittal T2-weighted MRI. The method combines contrastive pretraining with disc-level fine-tuning, using a single anatomically localized region of interest per intervertebral disc. Contrastive learning is employed to help the model focus on meaningful disc features and reduce sensitivity to irrelevant differences in image appearance. The framework includes an auxiliary regression task for disc localization and applies weighted focal loss to address class imbalance. Experiments demonstrate a 78.1% balanced accuracy and a reduced severe-to-normal misclassification rate of 2.13% compared with supervised training from scratch. Detecting discs with moderate severity can still be challenging, but focusing on disc-level features provides a practical way to assess the lumbar spinal stenosis.

翻译：本研究探讨了一种基于椎间盘中心的自动化腰椎管狭窄严重程度分级方法，该方法使用矢状面T2加权磁共振成像。该方法将对比预训练与椎间盘级别的微调相结合，每个椎间盘仅使用一个解剖定位的感兴趣区域。通过对比学习使模型专注于有意义的椎间盘特征，并降低对图像外观中无关差异的敏感性。该框架包含用于椎间盘定位的辅助回归任务，并应用加权焦点损失以处理类别不平衡问题。实验结果表明，与从头开始的监督训练相比，该方法实现了78.1%的平衡准确率，并将重度至正常的误分类率降低至2.13%。检测中度严重程度的椎间盘仍具挑战性，但聚焦于椎间盘级别的特征为评估腰椎管狭窄提供了一种实用途径。