Deep OCT angiography image generation for motion artifact suppression

Hoßbach J, Husvogt L, Kraus M, Fujimoto JG, Maier A (2020)


Publication Type: Conference contribution

Publication year: 2020

Journal

Publisher: Springer

Pages Range: 248-253

Conference Proceedings Title: Informatik aktuell

Event location: Berlin DE

ISBN: 9783658292669

DOI: 10.1007/978-3-658-29267-6_55

Abstract

Eye movements, blinking and other motion during the acquisition of optical coherence tomography (OCT) can lead to artifacts, when processed to OCT angiography (OCTA) images. Affected scans emerge as high intensity (white) or missing (black) regions, resulting in lost information. The aim of this research is to fill these gaps using a deep generative model for OCT to OCTA image translation relying on a single intact OCT scan. Therefore, a U-Net is trained to extract the angiographic information from OCT patches. At inference, a detection algorithm finds outlier OCTA scans based on their surroundings, which are then replaced by the trained network. We show that generative models can augment the missing scans. The augmented volumes could then be used for 3-D segmentation or increase the diagnostic value.

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How to cite

APA:

Hoßbach, J., Husvogt, L., Kraus, M., Fujimoto, J.G., & Maier, A. (2020). Deep OCT angiography image generation for motion artifact suppression. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik aktuell (pp. 248-253). Berlin, DE: Springer.

MLA:

Hoßbach, Julian, et al. "Deep OCT angiography image generation for motion artifact suppression." Proceedings of the International workshop on Algorithmen - Systeme - Anwendungen, 2020, Berlin Ed. Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm, Springer, 2020. 248-253.

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