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International MICCAI Brainlesion Workshop

BrainLes 2018: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries pp 234–244Cite as

No New-Net

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11384)

Abstract

In this paper we demonstrate the effectiveness of a well trained U-Net in the context of the BraTS 2018 challenge. This endeavour is particularly interesting given that researchers are currently besting each other with architectural modifications that are intended to improve the segmentation performance. We instead focus on the training process arguing that a well trained U-Net is hard to beat. Our baseline U-Net, which has only minor modifications and is trained with a large patch size and a Dice loss function indeed achieved competitive Dice scores on the BraTS2018 validation data. By incorporating additional measures such as region based training, additional training data, a simple postprocessing technique and a combination of loss functions, we obtain Dice scores of 77.88, 87.81 and 80.62, and Hausdorff Distances (95th percentile) of 2.90, 6.03 and 5.08 for the enhancing tumor, whole tumor and tumor core, respectively on the test data. This setup achieved rank two in BraTS2018, with more than 60 teams participating in the challenge.

Keywords

  • CNN
  • Brain tumor
  • Glioblastoma
  • U-Net
  • Dice loss

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Authors and Affiliations

  1. Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Fabian Isensee & Klaus H. Maier-Hein

  2. Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany

    Philipp Kickingereder & Martin Bendszus

  3. Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany

    Wolfgang Wick

Authors
  1. Fabian Isensee
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  2. Philipp Kickingereder
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  3. Wolfgang Wick
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  4. Martin Bendszus
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  5. Klaus H. Maier-Hein
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Corresponding author

Correspondence to Fabian Isensee .

Editor information

Editors and Affiliations

  1. University Hospital of Zurich, Zürich, Switzerland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

  3. University Medical Center Utrecht, Utrecht, The Netherlands

    Hugo Kuijf

  4. National Cancer Institute, Bethesda, MD, USA

    Farahani Keyvan

  5. University of Bern, Bern, Switzerland

    Mauricio Reyes

  6. Erasmus University Medical Center, Rotterdam, The Netherlands

    Theo van Walsum

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Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H. (2019). No New-Net. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science(), vol 11384. Springer, Cham. https://doi.org/10.1007/978-3-030-11726-9_21

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  • DOI: https://doi.org/10.1007/978-3-030-11726-9_21

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