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Detection and Segmentation of Brain Tumors from MRI Using U-Nets

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11993)

Abstract

In this paper, we exploit a cascaded U-Net architecture to perform detection and segmentation of brain tumors (low- and high-grade gliomas) from magnetic resonance scans. First, we detect tumors in a binary-classification setting, and they later undergo multi-class segmentation. The total processing time of a single input volume amounts to around 15  s using a single GPU. The preliminary experiments over the BraTS’19 validation set revealed that our approach delivers high-quality tumor delineation and offers instant segmentation.

Keywords

  • Brain tumor
  • Segmentation
  • Deep learning
  • CNN

We applied the sequence-determines-credit approach for the sequence of authors.

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Acknowledgments

This research was supported by the National Centre for Research and Development (POIR.01.02.00-00-0030/15). JN was supported by the Silesian University of Technology funds (02/020/BKM19/0183).

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Correspondence to Jakub Nalepa .

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Kotowski, K., Nalepa, J., Dudzik, W. (2020). Detection and Segmentation of Brain Tumors from MRI Using U-Nets. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11993. Springer, Cham. https://doi.org/10.1007/978-3-030-46643-5_17

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  • DOI: https://doi.org/10.1007/978-3-030-46643-5_17

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