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International MICCAI Brainlesion Workshop

BrainLes 2017: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries pp 344–357Cite as

Brain Tumor Segmentation Using Deep Fully Convolutional Neural Networks

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

Abstract

In this study, brain tumor substructures are segmented using 2D fully convolutional neural networks. A number of modifications such as double convolution layers, inception modules, and dense modules were added to a U-Net to achieve a deep architecture and test if the increased depth improves the performance. The experiments show that the deep architectures improve the performance. Also, the performance is enhanced from ensembling across the models trained on images in different orientations and ensembling across the models with different architectures. Even without any data augmentation, the ensembled model achieves a competitive performance and generalizes well on a new dataset. The resulting mean 3D Dice scores (ET/WT/TC) on the BRATS17 validation and test sets are 0.75/0.88/0.73 and 0.72/0.86/0.73.

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Acknowledgement

We thank NVIDIA for their kind donation of a TitanX GPU.

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Authors and Affiliations

  1. College of Computer and Information Sciences, Regis University, Denver, CO, USA

    Geena Kim

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  1. Geena Kim
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Correspondence to Geena Kim .

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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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Kim, G. (2018). Brain Tumor Segmentation Using Deep Fully 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_30

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  • DOI: https://doi.org/10.1007/978-3-319-75238-9_30

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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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