No New-Net

  • Fabian IsenseeEmail author
  • Philipp Kickingereder
  • Wolfgang Wick
  • Martin Bendszus
  • Klaus H. Maier-Hein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)


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.


CNN Brain tumor Glioblastoma U-Net Dice loss 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Fabian Isensee
    • 1
    Email author
  • Philipp Kickingereder
    • 2
  • Wolfgang Wick
    • 3
  • Martin Bendszus
    • 2
  • Klaus H. Maier-Hein
    • 1
  1. 1.Division of Medical Image ComputingGerman Cancer Research Center (DKFZ)HeidelbergGermany
  2. 2.Department of NeuroradiologyUniversity of Heidelberg Medical CenterHeidelbergGermany
  3. 3.Neurology ClinicUniversity of Heidelberg Medical CenterHeidelbergGermany

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