Deep Learning Retinal Vessel Segmentation from a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural Networks

  • Praneeth SaddaEmail author
  • John A. Onofrey
  • Xenophon Papademetris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11043)


Supervised deep learning methods such as fully convolutional neural networks have been very effective at medical image segmentation tasks. These approaches are limited, however, by the need for large amounts of labeled training data. The time and labor required for creating human-labeled ground truth segmentations for training examples is often prohibitive. This paper presents a method for the generation of synthetic examples using cyclic generative adversarial neural networks. The paper further shows that a fully convolutional network trained on a dataset of several synthetic examples and a single manually-crafted ground truth segmentation can approach the accuracy of an equivalent network trained on twenty manually segmented examples.


Deep learning Cyclic generative adversarial network Vessel segmentation 



This work was supported by the National Institutes of Health grant number T35DK104689 (NIDDK Medical Student Research Fellowship).


  1. 1.
    Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 2843–2851. Curran Associates, Inc. (2012)Google Scholar
  2. 2.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  3. 3.
    Hoover, A., Goldbaum, M.: Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Med. Imaging 22(8), 951–958 (2003)CrossRefGoogle Scholar
  4. 4.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  5. 5.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. CoRR abs/1505.04597 (2015).
  6. 6.
    Schroff, F., Criminisi, A., Zisserman, A.: Object class segmentation using random forests. In: Proceedings of the British Machine Vision Conference (BMVC) (2008)Google Scholar
  7. 7.
    Singh, A., Nowak, R., Zhu, X.: Unlabeled data: now it helps, now it doesn’t. In: Koller, D., Schuurmans, D., Bengio, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 21, pp. 1513–1520. Curran Associates, Inc. (2009).
  8. 8.
    Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)CrossRefGoogle Scholar
  9. 9.
    Wolterink, J.M., Dinkla, A.M., Savenije, M.H.F., Seevinck, P.R., van den Berg, C.A.T., Isgum, I.: Deep MR to CT synthesis using unpaired data. CoRR abs/1708.01155 (2017).
  10. 10.
    Zhou, Z.H.: A brief introduction to weakly supervised learning. Nat. Sci. Rev. 5(1), 44–53 (2018)CrossRefGoogle Scholar
  11. 11.
    Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. CoRR abs/1703.10593 (2017).

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Praneeth Sadda
    • 1
    Email author
  • John A. Onofrey
    • 1
    • 2
  • Xenophon Papademetris
    • 1
    • 2
    • 3
  1. 1.School of MedicineYale UniversityNew HavenUSA
  2. 2.Departments of Radiology and Biomedical ImagingYale UniversityNew HavenUSA
  3. 3.Biomedical EngineeringYale UniversityNew HavenUSA

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