Abstract
The goal of this study is to exploit feedback provided by experts that daily interact with a deep learning based segmentation tool to efficiently improve its segmentation performance. A convolutional neural network (CNN) for segmenting organs at risk for head & neck cancer was implemented in clinical practice and its predicted contours were verified, corrected and approved by the radiation oncologists before being used for treatment planning. A second CNN was subsequently trained to predict how the original contours as created by the first CNN should be adapted according to the experts. Three artificial datasets were created from the clinical dataset by introducing different amounts of systematic errors in the original predictions, in addition to the clinically corrected errors. Dice score for brainstem improved from 88% to 90.5% on average for the dataset that was the least adapted, and from 66% to 89.3% on average for the dataset in which the most systematic errors were introduced. For the clinical dataset, final segmentation improved for glottic area and supraglottic larynx compared to the initial predictions. In general, the systematic errors introduced in the contours are easier to learn compared to the clinical corrections by the expert, which are more subtle and subject to observer variability in our clinical dataset.
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Acknowledgments
Siri Willems and Heleen Bollen are supported by a Ph.D. fellowship of the research foundation - Flanders (FWO).
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Willems, S. et al. (2021). Learning from Mistakes: An Error-Driven Mechanism to Improve Segmentation Performance Based on Expert Feedback. In: Oyarzun Laura, C., et al. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning. DCL PPML LL-COVID19 CLIP 2021 2021 2021 2021. Lecture Notes in Computer Science(), vol 12969. Springer, Cham. https://doi.org/10.1007/978-3-030-90874-4_7
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