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Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks

  • Guotai WangEmail author
  • Wenqi Li
  • Sébastien Ourselin
  • Tom Vercauteren
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10670)

Abstract

A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core. The cascade is designed to decompose the multi-class segmentation problem into a sequence of three binary segmentation problems according to the subregion hierarchy. The whole tumor is segmented in the first step and the bounding box of the result is used for the tumor core segmentation in the second step. The enhancing tumor core is then segmented based on the bounding box of the tumor core segmentation result. Our networks consist of multiple layers of anisotropic and dilated convolution filters, and they are combined with multi-view fusion to reduce false positives. Residual connections and multi-scale predictions are employed in these networks to boost the segmentation performance. Experiments with BraTS 2017 validation set show that the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for enhancing tumor core, whole tumor and tumor core, respectively. The corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and 0.7748, respectively.

Keywords

Brain tumor Convolutional neural network Segmentation 

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], Engineering and Physical Sciences Research Council (EPSRC) [NS/A000027/1], the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative), a UCL Overseas Research Scholarship, a UCL Graduate Research Scholarship, hardware donated by NVIDIA, and the Health Innovation Challenge Fund [HICF-T4-275, WT 97914], a parallel funding partnership between the Department of Health and Wellcome Trust.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Guotai Wang
    • 1
    • 2
    Email author
  • Wenqi Li
    • 1
    • 2
  • Sébastien Ourselin
    • 1
    • 2
  • Tom Vercauteren
    • 1
    • 2
  1. 1.Translational Imaging Group, CMICUniversity College LondonLondonUK
  2. 2.Wellcome/EPSRC Centre for Interventional and Surgical SciencesUCLLondonUK

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