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Automatic Brain Tumor Segmentation Using Convolutional Neural Networks with Test-Time Augmentation

  • Guotai WangEmail author
  • Wenqi Li
  • Sébastien Ourselin
  • Tom Vercauteren
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of data augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs’ performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation can achieve higher segmentation accuracy and obtain uncertainty estimation of the segmentation results.

Keywords

Brain tumor Convolutional neural network Segmentation Data augmentation 

Notes

Acknowledgements

We would like to thank the NiftyNet team. This work was supported through an Innovative Engineering for Health award by the Wellcome Trust [WT101957, WT97914, 203145Z/16/Z, 203148/Z/16/Z], Engineering and Physical Sciences Research Council (EPSRC) [NS/A000027/1, NS/A000049/1, NS/A000050/1], the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative), hardware donated by NVIDIA, and the Health Innovation Challenge Fund [HICF-T4-275].

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Guotai Wang
    • 1
    • 2
    Email author
  • Wenqi Li
    • 1
    • 2
  • Sébastien Ourselin
    • 1
  • Tom Vercauteren
    • 1
    • 2
  1. 1.School of Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK
  2. 2.Wellcome/EPSRC Centre for Interventional and Surgical SciencesUniversity College LondonLondonUK

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