Automated organ at risk (OAR) segmentation is crucial for radiation therapy planning in CT scans, but the generated contours by automated models can be inaccurate, potentially leading to treatment planning issues. The reasons for these inaccuracies could be varied, such as unclear organ boundaries or inaccurate ground truth due to annotation errors. To improve the model's performance, it is necessary to identify these failure cases during the training process and to correct them with some potential post-processing techniques. However, this process can be time-consuming, as traditionally it requires manual inspection of the predicted output. This paper proposes a method to automatically identify failure cases by setting a threshold for the combination of Dice and Hausdorff distances. This approach reduces the time-consuming task of visually inspecting predicted outputs, allowing for faster identification of failure case candidates. The method was evaluated on 20 cases of six different organs in CT images from clinical expert curated datasets. By setting the thresholds for the Dice and Hausdorff distances, the study was able to differentiate between various states of failure cases and evaluate over 12 cases visually. This thresholding approach could be extended to other organs, leading to faster identification of failure cases and thereby improving the quality of radiation therapy planning.

翻译：自动化的危及器官（OAR）分割对于CT扫描中的放射治疗计划至关重要，但自动化模型生成的轮廓可能不准确，进而导致治疗计划问题。这些不精确的原因可能多种多样，例如器官边界不清或标注误差导致的金标准不准确。为提升模型性能，需在训练过程中识别这些失败案例，并通过潜在的后处理技术加以修正。然而传统上需要人工检查预测输出，这一过程非常耗时。本文提出一种方法，通过为Dice距离和Hausdorff距离的组合设置阈值，自动识别失败案例。该方法减少了人工检查预测输出的耗时任务，能够更快地识别出潜在的失败案例。该研究采用临床专家精选的CT图像数据集，对六种不同器官的20个案例进行了评估。通过设置Dice距离和Hausdorff距离的阈值，该方法能够区分不同状态的失败案例，并对超过12个案例进行了视觉验证。这种阈值方法可推广至其他器官，从而实现更快速的失败案例识别，进而提升放射治疗计划的质量。