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Quality assurance for automatically generated contours with additional deep learning
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(2022) Quality assurance for automatically generated contours with additional deep learning. Insights into Imaging, 13 (1). 137. doi:10.1186/s13244-022-01276-7 ISSN 1869-4101.
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Official URL: https://doi.org/10.1186/s13244-022-01276-7
Abstract
Objective:
Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model’s use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours.
Methods:
The model was trained to predict the segmentation quality by outputting an estimate of the Dice similarity coefficient given an image contour pair as input. Our dataset contained 60 axial T2-weighted MRI images of prostates with ground truth segmentations along with 80 automatically generated segmentation masks. The model we used was a 3D version of the EfficientDet architecture with a custom regression head. For validation, we used a fivefold cross-validation. To counteract the limitation of the small dataset, we used an extensive data augmentation scheme capable of producing virtually infinite training samples from a single ground truth label mask. In addition, we compared the results against a baseline model that only uses clinical variables for its predictions.
Results:
Our model achieved a mean absolute error of 0.020 ± 0.026 (2.2% mean percentage error) in estimating the Dice score, with a rank correlation of 0.42. Furthermore, the model managed to correctly identify incorrect segmentations (defined in terms of acceptable/unacceptable) 99.6% of the time.
Conclusion:
We believe that the trained model can be used alongside automatic segmentation tools to ensure quality and thus allow intervention to prevent undesired segmentation behavior.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RC Internal medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Diagnostic imaging -- Data processing, Image segmentation, Deep learning (Machine learning), Magnetic resonance imaging -- Digital techniques | ||||||
Journal or Publication Title: | Insights into Imaging | ||||||
Publisher: | Springer Vienna | ||||||
ISSN: | 1869-4101 | ||||||
Official Date: | 17 August 2022 | ||||||
Dates: |
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Volume: | 13 | ||||||
Number: | 1 | ||||||
Article Number: | 137 | ||||||
DOI: | 10.1186/s13244-022-01276-7 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 11 October 2022 | ||||||
Date of first compliant Open Access: | 11 October 2022 | ||||||
RIOXX Funder/Project Grant: |
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