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International Workshop on Multimodal Brain Image Analysis

International Workshop on Mathematical Foundations of Computational Anatomy

MBIA 2019, MFCA 2019: Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy pp 130–138Cite as

Multimodal Brain Tumor Segmentation Using Encoder-Decoder with Hierarchical Separable Convolution

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11846))

Abstract

To address automatic segmentation of brain tumor from multi-modal MRI volumes, a light-weight encoder-decoder network is presented. Exploring effective way to trade off the range of spatial contexts and computational efficiency is crucial to address challenges of 3D segmentation. To this end, we introduce hierarchical separable convolution (HSC), an integration of view- and group-wise separable convolution, which can simultaneously encode multi-scale context in 3D and reduce memory overhead without sacrificing accuracy. Specifically, typical 3D convolution is replaced with complementary 2D convolutions at multiple scales and thus multiple fields-of-view, which results in a light-weight but stronger model. Moreover, thanks to the decomposed convolutions, we ensemble 3D segmentations with different focal views to further improve segmentation accuracy. Experiments on the BRATS 2017 benchmark showed that our method achieved state-of-the-art performance in Dice, i.e., 0.901, 0.809 and 0.762 for the whole tumor, tumor core and enhancing tumor core, respectively.

Supported by National Natural Science Foundation of China (11771160) and Fujian Science and Technology Project (2019H0016).

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Notes

  1. 1.

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

  2. 2.

    http://www.tensorflow.org.

  3. 3.

    http://niftynet.io.

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Authors and Affiliations

  1. College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China

    Zhongdao Jia, Zhimin Yuan & Jialin Peng

Authors
  1. Zhongdao Jia
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  2. Zhimin Yuan
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  3. Jialin Peng
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Corresponding author

Correspondence to Jialin Peng .

Editor information

Editors and Affiliations

  1. The University of Texas at Arlington, Arlington, TX, USA

    Dajiang Zhu

  2. Indiana University – Purdue University Indianapolis, Indianapolis, IN, USA

    Jingwen Yan

  3. University of Pittsburgh, Pittsburgh, PA, USA

    Heng Huang

  4. University of Pennsylvania, Philadelphia, PA, USA

    Li Shen

  5. University of Southern California, Marina Del Rey, CA, USA

    Paul M. Thompson

  6. Harvard Medical School, Boston, MA, USA

    Carl-Fredrik Westin

  7. Inria Sophia-Antipolis, Sophia-Antipolis, France

    Xavier Pennec

  8. University of Utah, Salt Lake City, UT, USA

    Sarang Joshi

  9. University of Copenhagen, Copenhagen, Denmark

    Mads Nielsen

  10. University of Virginia, Charlottesville, VA, USA

    Tom Fletcher

  11. Inria, Paris, France

    Stanley Durrleman

  12. University of Copenhagen, Copenhagen, Denmark

    Stefan Sommer

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Jia, Z., Yuan, Z., Peng, J. (2019). Multimodal Brain Tumor Segmentation Using Encoder-Decoder with Hierarchical Separable Convolution. In: Zhu, D., et al. Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy. MBIA MFCA 2019 2019. Lecture Notes in Computer Science(), vol 11846. Springer, Cham. https://doi.org/10.1007/978-3-030-33226-6_15

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  • DOI: https://doi.org/10.1007/978-3-030-33226-6_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33225-9

  • Online ISBN: 978-3-030-33226-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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