Comparing Bayesian models for organ contouring in head and neck radiotherapy

@inproceedings{Mody2022ComparingBM,
  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},
  year={2022}
}
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… 
2 Citations

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References

SHOWING 1-10 OF 29 REFERENCES

Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

A 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice is demonstrated, suggesting this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy

An end-to-end, atlas-free three-dimensional convolutional deep learning framework for fast and fully automated whole-volume HaN anatomy segmentation and demonstrates that the proposed model can improve segmentation accuracy and simplify the autosegmentation pipeline.

A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images

A novel hybrid CNN is proposed that fuses 2D and 3D convolutions to combat the different spatial resolutions and extract effective edge and semantic features from 3D HaN CT images.

3D Variation in delineation of head and neck organs at risk

Variation in delineation is traced to several regional causes and measures to reduce this variation can be: (1) guideline development, (2) joint delineation review sessions and (3) application of multimodality imaging.

Evaluation of segmentation methods on head and neck CT: Auto‐segmentation challenge 2015

The results demonstrate a clear tendency toward more general purpose and fewer structure‐specific segmentation algorithms in the state‐of‐the‐art in segmentation of organs at risk for radiotherapy treatment.

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.

We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric Uncertainty.

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.

Interobserver variability in delineation of target volumes in head and neck cancer.