TY - GEN
T1 - Automatic Identification of Segmentation Errors for Radiotherapy Using Geometric Learning
AU - Henderson, Edward G. A.
AU - Green, Andrew F.
AU - van Herk, Marcel
AU - Vasquez Osorio, Eliana M.
N1 - Funding Information: Acknowledgements. Marcel van Herk was supported by NIHR Manchester Biomedical Research Centre. This work was also supported by Cancer Research UK via funding to the Cancer Research Manchester Centre [C147/A25254] and by Cancer Research UK RadNet Manchester [C1994/A28701]. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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 was trained using data-efficient learning using a synthetically-generated dataset of segmentations of the parotid gland with realistic contouring errors. The effectiveness of our model was assessed with ablation tests, evaluating the efficacy of different portions of the architecture as well as the use of transfer learning from a custom 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.
AB - 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 was trained using data-efficient learning using a synthetically-generated dataset of segmentations of the parotid gland with realistic contouring errors. The effectiveness of our model was assessed with ablation tests, evaluating the efficacy of different portions of the architecture as well as the use of transfer learning from a custom 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.
KW - Data-efficient learning
KW - Geometric learning
KW - Segmentation error detection
UR - http://www.scopus.com/inward/record.url?scp=85139064626&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-16443-9_31
DO - https://doi.org/10.1007/978-3-031-16443-9_31
M3 - Conference contribution
SN - 9783031164422
VL - 13435 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 319
EP - 329
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
A2 - Wang, Linwei
A2 - Dou, Qi
A2 - Fletcher, P. Thomas
A2 - Speidel, Stefanie
A2 - Li, Shuo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 22 September 2022
ER -