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SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12965)

Abstract

Fundus fluorescein angiography (FA) is an indispensable procedure that can investigate the integrity of retina vasculature. Fluorescein angiograms progress through five phases: pre-arterial, arterial, arteriovenous, venous, and late, and each phase could be an important diagnostic basis for retina-related disease. However, the FA imaging technique may provide risks of harm to the patients. To help physicians reduce the potential risks of diagnosis, we proposed “SequenceGAN”, a novel sequential generative adversarial network that aims to generate FA sequences of critical phases from a structure fundus image. Moreover, a feature-space loss is applied to ensure the generated FA sequences with a better visual effect. The proposed method was qualitatively and quantitatively compared with existing FA image generation methods and image translation methods. The experimental results indicate that the proposed model has better performance on the generation of retina vascular, leakage structures, and characteristics of each angiogram phase, and thus indicates potential value for application in clinical diagnosis.

Keywords

  • Fundus fluorescein angiography
  • Sequential generative adversarial network
  • Structure fundus image
  • Feature-space loss

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Acknowledgement

This work is supported by the National Key Research and Development Program of China (2016YFF0102002) and the National Natural Science Foundation of China (61605210, 62075235).

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Li, W. et al. (2021). SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image. In: Svoboda, D., Burgos, N., Wolterink, J.M., Zhao, C. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2021. Lecture Notes in Computer Science(), vol 12965. Springer, Cham. https://doi.org/10.1007/978-3-030-87592-3_11

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  • DOI: https://doi.org/10.1007/978-3-030-87592-3_11

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