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Segmenting Brain Tumors from MRI Using Cascaded 3D U-Nets

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 12659)

Abstract

In this paper, we exploit a cascaded 3D U-Net architecture to perform detection and segmentation of brain tumors (low- and high-grade gliomas) from multi-modal magnetic resonance scans. First, we detect tumors in a binary-classification setting, and they later undergo multi-class segmentation. To provide high-quality generalization, we investigate several regularization techniques that help improve the segmentation performance obtained for the unseen scans, and benefit from the expert knowledge of a senior radiologist captured in a form of several post-processing routines. Our preliminary experiments elaborated over the BraTS’20 validation set revealed that our approach delivers high-quality tumor delineation.

Keywords

  • Brain tumor
  • Segmentation
  • Deep learning
  • U-Net

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  • DOI: 10.1007/978-3-030-72087-2_23
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Notes

  1. 1.

    These post-processing steps were co-designed in co-operation with a senior radiologist with 12 years of experience.

  2. 2.

    Our team name is FutureHealthcare.

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Acknowledgments

JN was supported by the Silesian University of Technology funds through the grant for maintaining and developing research potential, and by the Rector’s Research and Development Grant (02/080/RGJ20/0003).

This paper is in memory of Dr. Grzegorz Nalepa, an extraordinary scientist and pediatric hematologist/oncologist at Riley Hospital for Children, Indianapolis, USA, who helped countless patients and their families through some of the most challenging moments of their lives.

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Kotowski, K., Adamski, S., Malara, W., Machura, B., Zarudzki, L., Nalepa, J. (2021). Segmenting Brain Tumors from MRI Using Cascaded 3D U-Nets. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_23

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  • DOI: https://doi.org/10.1007/978-3-030-72087-2_23

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