Deep OCT Angiography Image Generation for Motion Artifact Suppression
Name
2001.02512.pdf
Description
Submitted version
Size
1.18 MB
Format
Adobe PDF
Checksum (MD5)
4a806c9c565ca8eb5c822bdd7efc2343
Author(s)
Hossbach, Julian
Husvogt, Lennart
Kraus, Martin F.
Fujimoto, James G
Maier, Andreas K.
Date Issued
February 2020
Journal
Informatik aktuell
Publisher
Springer Fachmedien Wiesbaden
Citation
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
Version
Original manuscript
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.
Description
Part of the Informatik aktuell book series (INFORMAT)
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-658-29267-6_55
