Abstract
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.
Abstract (translated)
研究目的：这项研究提出了一种基于深度学习（DL）的质量评估（QA）方法，用于评价放射治疗中自动生成的轮廓（auto-contours），特别强调了在线自适应放射治疗（OART）。该方法利用贝叶斯序值分类（BOC）和校准后的不确定性阈值，在没有依赖地面真实轮廓或大量手动标记的情况下，能够提供自信的质量评估预测。 研究方法：我们开发了一种BOC模型来对auto-contour质量进行分类，并量化预测的不确定性。通过优化符合临床精度需求的不确定性的阈值进行校正。该方法在三种数据场景下进行了验证：没有手动标签、有限的手动标签和大量的手动标签。对于前列腺癌患者的直肠轮廓，当缺少手动标签时我们应用几何替代标签；当标记数量有限时使用迁移学习；当有足够多的手动标签时则直接进行监督训练。 研究结果：BOC模型在所有场景中都表现出色。通过仅用30个手动标签微调，并对34个受试者的数据进行校准，测试数据的准确率超过90%。利用校正后的阈值，在超过98%的情况下能够准确预测超过93%的auto-contours的质量，这减少了不必要的手动审查并突出需要修正的情况。 结论：所提出的QA模型通过减少手工工作量并支持快速、有信息支撑的临床决策，提高了OART中轮廓绘制的效率。通过不确定性量化，它确保了更安全且可靠的放射治疗流程。
URL
https://arxiv.org/abs/2505.00308
