Quantification of uncertainty in brain tumor segmentation using generative network and bayesian active learning

Rasha Alshehhi, Anood Alshehhi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Convolutional neural networks have shown great potential in medical segmentation problems, such as braintumor segmentation. However, little consideration has been given to generative adversarial networks and uncertainty quantification over the output images. In this paper, we use the generative adversarial network to handle limited labeled images. We also quantify the modeling uncertainty by utilizing Bayesian active learning to reduce untoward outcomes. Bayesian active learning is dependent on selecting uncertain images using acquisition functions to increase accuracy. We introduce supervised acquisition functions based on distance functions between ground-truth and predicted images to quantify segmentation uncertainty. We evaluate the method by comparing it with the state-of-the-art methods based on Dice score, Hausdorff distance and sensitivity. We demonstrate that the proposed method achieves higher or comparable performance to state-of-the-art methods for brain tumor segmentation (on BraTS 2017, BraTS 2018 and BraTS 2019 datasets).

Original languageEnglish (US)
Title of host publicationVISAPP
EditorsGiovanni Maria Farinella, Petia Radeva, Jose Braz, Kadi Bouatouch
PublisherSciTePress
Pages701-709
Number of pages9
ISBN (Electronic)9789897584886
StatePublished - 2021
Event16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021 - Virtual, Online
Duration: Feb 8 2021Feb 10 2021

Publication series

NameVISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Volume4

Conference

Conference16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021
CityVirtual, Online
Period2/8/212/10/21

Keywords

  • Bayesian Active Learning
  • Generative Adversarial Network
  • Segmentation
  • Uncertainty

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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