  Automatic segmentation of organs-at-risk (OARs) in CT scans using
convolutional neural networks (CNNs) is being introduced into the radiotherapy
workflow. However, these segmentations still require manual editing and
approval by clinicians prior to clinical use, which can be time consuming. The
aim of this work was to develop a tool to automatically identify errors in 3D
OAR segmentations without a ground truth. Our tool uses a novel architecture
combining a CNN and graph neural network (GNN) to leverage the segmentation's
appearance and shape. The proposed model is trained using self-supervised
learning using a synthetically-generated dataset of segmentations of the
parotid and with realistic contouring errors. The effectiveness of our model is
assessed with ablation tests, evaluating the efficacy of different portions of
the architecture as well as the use of transfer learning from an unsupervised
pretext task. Our best performing model predicted errors on the parotid gland
with a precision of 85.0% & 89.7% for internal and external errors
respectively, and recall of 66.5% & 68.6%. This offline QA tool could be used
in the clinical pathway, potentially decreasing the time clinicians spend
correcting contours by detecting regions which require their attention. All our
code is publicly available at
https://github.com/rrr-uom-projects/contour_auto_QATool.
