Comparing Bayesian models for organ contouring in head and neck radiotherapy
| Autor: | Prerak Mody, Nicolas Chaves-de-Plaza, Klaus Hildebrandt, Rene van Egmond, Huib de Ridder, Marius Staring |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Radiotherapy DropOut Computer Vision and Pattern Recognition (cs.CV) Entropy Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition Uncertainty Electrical Engineering and Systems Science - Image and Video Processing Bayesian Deep Learni Machine Learning (cs.LG) Segmentation FlipOut FOS: Electrical engineering electronic engineering information engineering |
| Zdroj: | Progress in Biomedical Optics and Imaging, 12032 |
| ISSN: | 1605-7422 |
| Popis: | Deep learning models for organ contouring in radiotherapy are poised for clinical usage, but currently, there exist few tools for automated quality assessment (QA) of the predicted contours. Using Bayesian models and their associated uncertainty, one can potentially automate the process of detecting inaccurate predictions. We investigate two Bayesian models for auto-contouring, DropOut and FlipOut, using a quantitative measure - expected calibration error (ECE) and a qualitative measure - region-based accuracy-vs-uncertainty (R-AvU) graphs. It is well understood that a model should have low ECE to be considered trustworthy. However, in a QA context, a model should also have high uncertainty in inaccurate regions and low uncertainty in accurate regions. Such behaviour could direct visual attention of expert users to potentially inaccurate regions, leading to a speed up in the QA process. Using R-AvU graphs, we qualitatively compare the behaviour of different models in accurate and inaccurate regions. Experiments are conducted on the MICCAI2015 Head and Neck Segmentation Challenge and on the DeepMindTCIA CT dataset using three models: DropOut-DICE, Dropout-CE (Cross Entropy) and FlipOut-CE. Quantitative results show that DropOut-DICE has the highest ECE, while Dropout-CE and FlipOut-CE have the lowest ECE. To better understand the difference between DropOut-CE and FlipOut-CE, we use the R-AvU graph which shows that FlipOut-CE has better uncertainty coverage in inaccurate regions than DropOut-CE. Such a combination of quantitative and qualitative metrics explores a new approach that helps to select which model can be deployed as a QA tool in clinical settings. 10 pages, 5 figures, To be published in "SPIE Medical Imaging 2022" |
| Databáze: | OpenAIRE |
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