U-Net: Convolutional Networks for Biomedical Image Segmentation (original) (raw)

Abstract

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .

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Authors and Affiliations

  1. Computer Science Department and BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
    Olaf Ronneberger, Philipp Fischer & Thomas Brox

Authors

  1. Olaf Ronneberger
  2. Philipp Fischer
  3. Thomas Brox

Corresponding author

Correspondence toOlaf Ronneberger .

Editor information

Editors and Affiliations

  1. TU München, Garching, Germany
    Nassir Navab
  2. Lehrstuhl Informatik 5, University of Erlangen-Nuremberg, Erlangen, Germany
    Joachim Hornegger
  3. Medical School, Brigham & Women’s Hospital Harvard, Boston, USA
    William M. Wells
  4. Electronic & Electrical Eng, University of Sheffield, Sheffield, United Kingdom
    Alejandro F. Frangi

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© 2015 Springer International Publishing Switzerland

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Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4\_28

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