Synthetic fundus fluorescein angiography using deep neural networks
Schiffers F, Yu Z, Arguin S, Maier A, Ren Q (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Berlin Heidelberg
Pages Range: 234-238
Conference Proceedings Title: Informatik aktuell
Event location: Erlangen, DEU
DOI: 10.1007/978-3-662-56537-7_64
Abstract
Fundus fluorescein angiography yields complementary image information when compared to conventional fundus imaging. Angiographic imaging, however, may pose risks of harm to the patient. The output from both types of imaging have different characteristics, but the most prominent features of the fundus are shared in both images. Thus, the question arises if conventional fundus images alone provide enough information to synthesize an angiographic image. Our research analyzes the capacity of deep neural networks to synthesize virtual angiographic images from their conventional fundus counterparts.
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China (CN)How to cite
APA:
Schiffers, F., Yu, Z., Arguin, S., Maier, A., & Ren, Q. (2018). Synthetic fundus fluorescein angiography using deep neural networks. In Heinz Handels, Thomas Tolxdorff, Thomas M. Deserno, Klaus H. Maier-Hein, Andreas Maier, Christoph Palm (Eds.), Informatik aktuell (pp. 234-238). Erlangen, DEU: Springer Berlin Heidelberg.
MLA:
Schiffers, Florian, et al. "Synthetic fundus fluorescein angiography using deep neural networks." Proceedings of the Workshop on Bildverarbeitung fur die Medizin, 2018, Erlangen, DEU Ed. Heinz Handels, Thomas Tolxdorff, Thomas M. Deserno, Klaus H. Maier-Hein, Andreas Maier, Christoph Palm, Springer Berlin Heidelberg, 2018. 234-238.
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