聚焦椎间盘特征，提升腰椎管狭窄自动分级准确率
Disc-Centric Contrastive Learning for Lumbar Spine Severity Grading
- 以每个椎间盘为单位定位兴趣区，结合对比学习提取关键特征
- 平衡准确率达78.1%，严重误判率降至2.13%（较传统训练降低）
- 适合医学影像分析、脊柱疾病智能诊断场景使用
本研究提出一种以椎间盘为中心的自动化腰椎管狭窄严重程度分级方法，基于矢状面T2加权MRI图像。该方法采用对比预训练结合椎间盘级微调，每椎间盘仅使用一个解剖学定位的感兴趣区域。通过对比学习引导模型关注有意义的椎间盘特征，减少对图像外观无关差异的敏感性。框架包含辅助回归任务用于椎间盘定位，并采用加权焦点损失缓解类别不平衡问题。实验表明，该方法达到78.1%的平衡准确率，严重程度误判率降低至2.13%，相较于从头监督训练有显著提升。虽然中度严重程度椎间盘的检测仍具挑战，但聚焦椎间盘特征提供了一种切实可行的腰椎管狭窄评估路径。
原文摘要 · Abstract (English)
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.
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