用深度学习自动评估放疗轮廓质量，无需人工标注也能精准判断。
AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality
- 基于贝叶斯序数分类与不确定性校准，实现无真值依赖的轮廓质量评估。
- 仅需30个标注样本微调，34个样本校准，测试准确率超90%。
- 适合放疗科医生快速筛选高风险轮廓，提升在线自适应放疗效率。
目的：提出一种基于深度学习的质量评估（QA）方法，用于评估放射治疗中自动生成的轮廓（auto-contours），重点关注在线自适应放疗（OART）。该方法结合贝叶斯序数分类（BOC）与校准的不确定性阈值，可在无需真值轮廓或大量人工标注的情况下实现可信的预测。方法：构建了BOC模型以分类轮廓质量并量化预测不确定性；通过校准步骤优化满足临床需求的不确定性阈值。在三种数据场景下验证：无手动标签、标签有限和标签充足。针对前列腺癌患者的直肠轮廓，在无手动标签时使用几何替代标签，标签有限时采用迁移学习，标签充足时直接监督训练。结果：BOC模型在所有场景下表现稳健。仅用30个手动标签微调，34个受试者校准后，测试集准确率超过90%。使用校准阈值后，98%的病例中超过93%的轮廓质量被准确预测，显著减少不必要的手动审查，并有效识别需修正的案例。结论：所提QA模型通过降低人工工作量，加速临床决策，提升OART中的轮廓绘制效率与安全性。
原文摘要 · Abstract (English)
Purpose: This study presents a Deep Learning (DL)-based quality assessment (QA) approach for evaluating auto-generated contours (auto-contours) in radiotherapy, with emphasis on Online Adaptive Radiotherapy (OART). Leveraging Bayesian Ordinal Classification (BOC) and calibrated uncertainty thresholds, the method enables confident QA predictions without relying on ground truth contours or extensive manual labeling. Methods: We developed a BOC model to classify auto-contour quality and quantify prediction uncertainty. A calibration step was used to optimize uncertainty thresholds that meet clinical accuracy needs. The method was validated under three data scenarios: no manual labels, limited labels, and extensive labels. For rectum contours in prostate cancer, we applied geometric surrogate labels when manual labels were absent, transfer learning when limited, and direct supervision when ample labels were available. Results: The BOC model delivered robust performance across all scenarios. Fine-tuning with just 30 manual labels and calibrating with 34 subjects yielded over 90% accuracy on test data. Using the calibrated threshold, over 93% of the auto-contours' qualities were accurately predicted in over 98% of cases, reducing unnecessary manual reviews and highlighting cases needing correction. Conclusion: The proposed QA model enhances contouring efficiency in OART by reducing manual workload and enabling fast, informed clinical decisions. Through uncertainty quantification, it ensures safer, more reliable radiotherapy workflows.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。