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Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10670))

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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.

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Notes

  1. 1.

    http://www.med.upenn.edu/sbia/brats2017.html.

  2. 2.

    https://www.tensorflow.org.

  3. 3.

    http://niftynet.io.

  4. 4.

    https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/tree/dev/demos/BRATS17.

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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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Authors and Affiliations

  1. Translational Imaging Group, CMIC, University College London, London, UK

    Guotai Wang, Wenqi Li, Sébastien Ourselin & Tom Vercauteren

  2. Wellcome/EPSRC Centre for Interventional and Surgical Sciences, UCL, London, UK

    Guotai Wang, Wenqi Li, Sébastien Ourselin & Tom Vercauteren

Authors
  1. Guotai Wang
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  2. Wenqi Li
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  3. Sébastien Ourselin
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  4. Tom Vercauteren
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Corresponding author

Correspondence to Guotai Wang .

Editor information

Editors and Affiliations

  1. African Institute of Mathematical Sciences, Cape Town, Ghana

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Spyridon Bakas

  3. University Medical Center Utrecht, Utrecht, The Netherlands

    Hugo Kuijf

  4. Technical University Munich, Munich, Germany

    Bjoern Menze

  5. Universität Bern, Bern, Switzerland

    Mauricio Reyes

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Wang, G., Li, W., Ourselin, S., Vercauteren, T. (2018). Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_16

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  • DOI: https://doi.org/10.1007/978-3-319-75238-9_16

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