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Deep OCT Angiography Image Generation for Motion Artifact Suppression

  • Julian HossbachEmail author
  • Lennart Husvogt
  • Martin F. Kraus
  • James G. Fujimoto
  • Andreas K. Maier
Conference paper
  • 55 Downloads
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

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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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Julian Hossbach
    • 1
    • 2
    Email author
  • Lennart Husvogt
    • 1
    • 3
  • Martin F. Kraus
    • 1
    • 4
  • James G. Fujimoto
    • 3
  • Andreas K. Maier
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenDeutschland
  2. 2.SAOTFriedrich-Alexander-Universität ErlangenErlangenDeutschland
  3. 3.Biomedical Optical Imaging and Biophotonics GroupMITCambridgeUSA
  4. 4.Siemens Healthineers AGErlangenDeutschland

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