One Class Classification-based Quality Assurance of Organs-at-risk Delineation in Radiotherapy

Yihao Zhao, Cuiyun Yuan, Ying Liang, Yang Li, Chunxia Li, Man Zhao, Jun Hu, Ningze Zhong, Chenbin Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4898-4906

The delineation of tumor target and organs-at-risk (OARs) is critical in the radiotherapy treatment planning. It is also tedious time-consuming and prone to subjective experiences. Automatic segmentation can be used to reduce the physician's workload. However the quality assurance of the segmentation is an unmet need in clinical practice. In this study we developed an automatic model that detects the errors of the contouring using one-class classifier. The OARs included left and right lungs heart esophagus and spinal cord. Each data includes the ground truth which is manually contoured by experienced doctor and contour generated by a contouring software. We used three metrics to determine whether the contour of an OAR is "high" or "low" quality. A resnet-152 network performed as a feature extractor and a one class support vector machine determines the quality of the contour. We generated certain contour errors to evaluate the generalizability of this method. Furthermore to enhance the interpretability of this method we conducted a set of experiments to assess its detection limit and discussed the correlation between this limit and metrics such as volume DSC HD95 and MSD. The proposed method showed significant improvement over binary classifiers in handling various types of errors. The relationship between the detection limit and multiple factors of the OARs indicates that our method is highly interpretable. Moreover the model's fast execution speed can significantly reduce the burden on physicians.