Deep OCT Angiography Image Generation for Motion Artifact Suppression
Author(s)Hossbach, Julian; Husvogt, Lennart; Kraus, Martin F.; Fujimoto, James G; Maier, Andreas K.
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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.
Part of the Informatik aktuell book series (INFORMAT)
DepartmentMassachusetts Institute of Technology. Research Laboratory of Electronics; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Springer Fachmedien Wiesbaden
Hossbach, Julian et al. "Deep OCT Angiography Image Generation for Motion Artifact Suppression." Bildverarbeitung für die Medizin 2020, Informatik aktuell, Springer Fachmedien Wiesbaden, 2020, 248-253. © 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature