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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)

Abstract

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.

Keywords

Deep learning Cyclic generative adversarial network Vessel segmentation 

Notes

Acknowledgements

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

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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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