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Dilated Convolutions for Brain Tumor Segmentation in MRI Scans

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

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Abstract

We present a novel method to detect and segment brain tumors in Magnetic Resonance Imaging scans using a novel network based on the Dilated Residual Network. Dilated convolutions provide efficient multi-scale analysis for dense prediction tasks without losing resolution by downsampling the input. To the best of our knowledge, our work is the first to evaluate a dilated residual network for brain tumor segmentation in magnetic resonance imaging scans. We train and evaluate our method on the Brain Tumor Segmentation (BraTS) 2017 challenge dataset. To address the severe label imbalance in the data, we adopt a balanced, patch-based sampling approach for training. An ablation study establishes the importance of residual connections in the performance of our network.

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Notes

  1. 1.

    This is actually a cross-correlation but we call it a convolution as is common in the literature today.

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Acknowledgments

We gratefully acknowledge the support of the UCCS Center of the BioFrontiers Institute, the Balsells Foundation, and National Science Foundation Grant No. 1659788.

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Authors and Affiliations

  1. Department of Computer Science, University of Colorado, Colorado Springs, USA

    Marc Moreno Lopez & Jonathan Ventura

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  1. Marc Moreno Lopez
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  2. Jonathan Ventura
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Correspondence to Jonathan Ventura .

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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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Moreno Lopez, M., Ventura, J. (2018). Dilated Convolutions for Brain Tumor Segmentation in MRI Scans. 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_22

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  • DOI: https://doi.org/10.1007/978-3-319-75238-9_22

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-75238-9

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