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Segmenting Brain Tumors from MRI Using Cascaded Multi-modal U-Nets

  • Michal Marcinkiewicz
  • Jakub NalepaEmail author
  • Pablo Ribalta Lorenzo
  • Wojciech Dudzik
  • Grzegorz Mrukwa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

Gliomas are the most common primary brain tumors, and their accurate manual delineation is a time- consuming and very user-dependent process. Therefore, developing automated techniques for reproducible detection and segmentation of brain tumors from magnetic resonance imaging is a vital research topic. In this paper, we present a deep learning-powered approach for brain tumor segmentation which exploits multiple magnetic-resonance modalities and processes them in two cascaded stages. In both stages, we use multi-modal fully-convolutional neural nets inspired by U-Nets. The first stage detects regions of interests, whereas the second stage performs the multi-class classification. Our experimental study, performed over the newest release of the BraTS dataset (BraTS 2018) showed that our method delivers accurate brain-tumor delineation and offers very fast processing—the total time required to segment one study using our approach amounts to around 18 s.

Keywords

Brain tumor Segmentation Deep learning CNN 

Notes

Acknowledgments

This research was supported by the National Centre for Research and Development under the Innomed Research and Development Grant No. POIR.01.02.00-00-0030/15.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Michal Marcinkiewicz
    • 1
  • Jakub Nalepa
    • 1
    • 2
    Email author
  • Pablo Ribalta Lorenzo
    • 2
  • Wojciech Dudzik
    • 1
    • 2
  • Grzegorz Mrukwa
    • 1
    • 2
  1. 1.Future ProcessingGliwicePoland
  2. 2.Silesian University of TechnologyGliwicePoland

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