Comparing Bayesian models for organ contouring in head and neck radiotherapy

  title={Comparing Bayesian models for organ contouring in head and neck radiotherapy},
  author={Prerak Mody and Nicolas F. Chaves-de-Plaza and Klaus Hildebrandt and Ren{\'e} van Egmond and Huib de Ridder and Marius Staring},
  booktitle={Medical Imaging},
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… 


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Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search
  • Dazhou GuoD. Jin Le Lu
  • Computer Science
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2020
Extensive 4-fold cross-validation on 142 H&N cancer patients with 42 manually labeled OARs, the most comprehensive OAR dataset to date, demonstrates that both pipeline- and NAS-stratification significantly improves quantitative performance over the state-of-the-art.
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A novel 3D Bayesian convolutional neural network (BCNN) is proposed, the first deep learning method which generates statistically credible geometric uncertainty maps and scales for application to 3D data.