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Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation

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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 deep learning approach to glioma segmentation is presented. An encoder and decoder pair deep learning network is designed which takes T1, T2, T1-CE (contrast enhanced) and T2-Flair (fluid attenuation inversion recovery) images as input and outputs the segmented labels. The encoder is a 49 layer deep residual learning architecture that encodes the \(240\,\times \,240\,\times \,4\) input images into \(8\,\times \,8\,\times \,2048\) feature maps. The decoder network takes these feature maps and extract the segmented labels. The decoder network is fully convolutional network consisting of convolutional and upsampling layers. Additionally, the input images are downsampled using bilinear interpolation and are inserted into the decoder network through concatenation. This concatenation step provides spatial information of the tumor to the decoder, which was lost due to pooling/downlsampling during encoding. The network is trained on the BRATS-17 training dataset and validated on the validation dataset. The dice score, sensitivity and specificity of the segmented whole tumor, core tumor and enhancing tumor is computed on validation dataset. The mean dice score for whole tumor, core tumor and enhancing tumor for validation dataset were 0.824, 0.627 and 0.575, respectively.

Caffe model and code available at: https://github.com/kamleshpawar17/BratsNet-2017.

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Author information

Authors and Affiliations

  1. Monash Biomedical Imaging, Monash University, Melbourne, Australia

    Kamlesh Pawar, Zhaolin Chen, N. Jon Shah & Gary Egan

  2. School of Psychological Sciences, Monash University, Melbourne, Australia

    Kamlesh Pawar & Gary Egan

  3. Electrical and Computer System Engineering, Monash University, Melbourne, Australia

    Zhaolin Chen

  4. Research Centre Juelich, Institute of Medicine, Juelich, Germany

    N. Jon Shah

Authors
  1. Kamlesh Pawar
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  2. Zhaolin Chen
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  3. N. Jon Shah
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  4. Gary Egan
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Corresponding author

Correspondence to Kamlesh Pawar .

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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Pawar, K., Chen, Z., Shah, N.J., Egan, G. (2018). Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation. 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_23

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  • DOI: https://doi.org/10.1007/978-3-319-75238-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75237-2

  • Online ISBN: 978-3-319-75238-9

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