International Conference on Medical Image Computing and Computer-Assisted Intervention

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 pp 234-241 | Cite as

U-Net: Convolutional Networks for Biomedical Image Segmentation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cardona, A., et al.: An integrated micro- and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol. 8(10), e1000502 (2010)Google Scholar
  2. 2.
    Ciresan, D.C., Gambardella, L.M., Giusti, A., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: NIPS, pp. 2852–2860 (2012)Google Scholar
  3. 3.
    Dosovitskiy, A., Springenberg, J.T., Riedmiller, M., Brox, T.: Discriminative unsupervised feature learning with convolutional neural networks. In: NIPS (2014)Google Scholar
  4. 4.
    Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization (2014), arXiv:1411.5752 [cs.CV]Google Scholar
  5. 5.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015), arXiv:1502.01852 [cs.CV]Google Scholar
  6. 6.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding (2014), arXiv:1408.5093 [cs.CV]Google Scholar
  7. 7.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)Google Scholar
  8. 8.
    LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Computation 1(4), 541–551 (1989)CrossRefGoogle Scholar
  9. 9.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation (2014), arXiv:1411.4038 [cs.CV]Google Scholar
  10. 10.
    Maska, M., et al.: A benchmark for comparison of cell tracking algorithms. Bioinformatics 30, 1609–1617 (2014)CrossRefGoogle Scholar
  11. 11.
    Seyedhosseini, M., Sajjadi, M., Tasdizen, T.: Image segmentation with cascaded hierarchical models and logistic disjunctive normal networks. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 2168–2175 (2013)Google Scholar
  12. 12.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014), arXiv:1409.1556 [cs.CV]Google Scholar
  13. 13.
  14. 14.
    WWW: Web page of the em segmentation challenge, http://brainiac2.mit.edu/isbi_challenge/

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Olaf Ronneberger
    • 1
  • Philipp Fischer
    • 1
  • Thomas Brox
    • 1
  1. 1.Computer Science Department and BIOSS Centre for Biological Signalling StudiesUniversity of FreiburgFreiburgGermany

Personalised recommendations