Comparing Bayesian models for organ contouring in head and neck radiotherapy

Prerak Mody*, Nicolas Chaves de Plaza, Klaus Hildebrandt, René van Egmond, Huib de Ridder, Marius Staring

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

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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. Bayesian models and their associated uncertainty, 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.
Original languageEnglish
Pages (from-to)120320F-1 - 120320F-10
Number of pages10
JournalProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Publication statusPublished - 2022
EventSPIE Medical Imaging 2022 - San Diego, United States
Duration: 20 Feb 202224 Feb 2022


  • Radiotherapy
  • Segmentation
  • Uncertainty
  • Bayesian Deep Learni
  • DropOut
  • FlipOut
  • Entropy


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