Purpose. Auto-contouring (AC) is rapidly becoming standard practice for OAR contouring. However, in clinical practice, clinicians still need to manually check and correct contours. Anomaly detection systems (ADS) can aid the clinical decision process by suggesting which structures require corrections or not, greatly enhancing the value of AC. The purpose of this work is to develop and evaluate a decision support system for detecting anomalies in the case of parotid gland delineations. Methods. Head and neck parotid gland delineations (1037 right, 1038 left), were retrieved from the Netherlands Cancer Institute (NKI) database. Morphological and image-based features were extracted from each patient’s CT and structure set. An isolation forest model was initially trained on 70% of the data, of which 10% had synthetically generated anomalies and validated on the remaining 30% of clinical data. The ADS was tested on an independent set of 250 patients (Normal: 174, Anomalies: 76) and on a clinical autocontouring software. Results. Applied to the validation set, the ADS system resulted in area under the curve (AUC) values of 0.93 and 0.94 for the parotid left and right respectively. Image features appeared more important than morphological, but using all features resulted marginally in the best model. Applied to the test set the ADS system reached an accuracy level of 0.83 and 0.81 for the parotid left and right respectively. The ADS was particularly sensitive to uniform expansions/contractions, misplacements, extra/missing slices and anisotropic over-contouring. Conclusion. Anomaly detection can serve as a powerful contour quality assurance tool, especially for cases of organ misplacement and over-contouring.

目的. 自动轮廓 (AC) 正迅速成为 OAR 轮廓的标准做法。然而，在临床实践中，临床医生仍然需要手动检查和校正轮廓。异常检测系统 (ADS) 可以通过建议哪些结构需要修正或不需要修正来帮助临床决策过程，大大提高了 AC 的价值。这项工作的目的是开发和评估用于检测腮腺轮廓异常的决策支持系统。方法。从荷兰癌症研究所 (NKI) 数据库检索头颈部腮腺轮廓（右 1037 个，左 1038 个）。从每个患者的 CT 和结构集中提取形态学和基于图像的特征。隔离森林模型最初是在 70% 的数据上训练的，其中 10% 已合成生成异常并在其余 30% 的临床数据上进行了验证。ADS 在一组独立的 250 名患者（正常：174，异常：76）和临床自动轮廓软件上进行了测试。结果。应用于验证集，ADS 系统导致腮腺左侧和右侧的曲线下面积 (AUC) 值分别为 0.93 和 0.94。图像特征似乎比形态学更重要，但使用所有特征只能得到最好的模型。应用于测试集的ADS系统分别对腮腺左侧和右侧达到了0.83和0.81的准确度水平。ADS 对均匀膨胀/收缩、错位、额外/缺失切片和各向异性过度轮廓特别敏感。结论。