Zusammenfassung
Medical imaging is often burdened with small available annotated data. In case of supervised deep learning algorithms a large amount of data is needed. One common strategy is to augment the given dataset for increasing the amount of training data. Recent researches show that the generation of synthetic images is a possible strategy to expand datasets. Especially, generative adversarial networks (GAN)s are promising candidates for generating new annotated training images. This work combines recent architectures of Generative Adversarial Networks in one pipeline to generate medical original and segmented image pairs for semantic segmentation. Results of training a U-Net with incorporated synthetic images as addition to common data augmentation are showing a performance boost compared to training without synthetic images from 77.99% to 80.23% average Jaccard Index.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
Literatur
Ronneberger O, Fischer P, Brox T; Springer. U-net: convolutional networks for biomedical image segmentation. Proc MICCAI. 2015; p. 234-241.
Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Adv Neural Inf Process Syst. 2014; p. 2672-2680.
Frid-Adar M, Diamant I, Klang E, et al. GAN-based synthetic medical image augmentation for increased CNN Performance in Liver Lesion Classification. CoRR. 2018;abs/1803.01229.
Arjovsky M, Chintala S, Bottou L. Wasserstein generative adversarial networks. Proc 34th Int Conf Mach Learn. 2017;70:214-223.
Zhang H, Xu T, Li H, et al. StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. CoRR. 2016;abs/1612.03242.
Wang T, Liu M, Zhu J, et al. High-resolution image synthesis and semantic manipulation with conditional GANs. CoRR. 2017;abs/1711.11585.
Gulrajani I, Ahmed F, Arjovsky M, et al. Improved training of Wasserstein gans. Adv Neural Inf Process Syst. 2017; p. 5769-5779.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Krauth, J., Gerlach, S., Marzahl, C., Voigt, J., Handels, H. (2019). Synthetic Training with Generative Adversarial Networks for Segmentation of Microscopies. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-658-25326-4_12
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-25325-7
Online ISBN: 978-3-658-25326-4
eBook Packages: Computer Science and Engineering (German Language)