Deep OCT Angiography Image Generation for Motion Artifact Suppression
Part of the Informatik aktuell book series (INFORMAT)
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Fachmedien Wiesbaden
2021
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| Online Access: | https://hdl.handle.net/1721.1/129342 |
| _version_ | 1826204137350496256 |
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| author | Hossbach, Julian Husvogt, Lennart Kraus, Martin F. Fujimoto, James G Maier, Andreas K. |
| author2 | Massachusetts Institute of Technology. Research Laboratory of Electronics |
| author_facet | Massachusetts Institute of Technology. Research Laboratory of Electronics Hossbach, Julian Husvogt, Lennart Kraus, Martin F. Fujimoto, James G Maier, Andreas K. |
| author_sort | Hossbach, Julian |
| collection | MIT |
| description | Part of the Informatik aktuell book series (INFORMAT) |
| first_indexed | 2024-09-23T12:49:29Z |
| format | Article |
| id | mit-1721.1/129342 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:49:29Z |
| publishDate | 2021 |
| publisher | Springer Fachmedien Wiesbaden |
| record_format | dspace |
| spelling | mit-1721.1/1293422022-10-01T11:21:02Z Deep OCT Angiography Image Generation for Motion Artifact Suppression Hossbach, Julian Husvogt, Lennart Kraus, Martin F. Fujimoto, James G Maier, Andreas K. Massachusetts Institute of Technology. Research Laboratory of Electronics Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Part of the Informatik aktuell book series (INFORMAT) 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. 2021-01-08T15:10:59Z 2021-01-08T15:10:59Z 2020-02 2020-12-14T20:29:15Z Article http://purl.org/eprint/type/ConferencePaper 9783658292669 9783658292676 1431-472X https://hdl.handle.net/1721.1/129342 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 en http://dx.doi.org/10.1007/978-3-658-29267-6_55 Informatik aktuell Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer Fachmedien Wiesbaden arXiv |
| spellingShingle | Hossbach, Julian Husvogt, Lennart Kraus, Martin F. Fujimoto, James G Maier, Andreas K. Deep OCT Angiography Image Generation for Motion Artifact Suppression |
| title | Deep OCT Angiography Image Generation for Motion Artifact Suppression |
| title_full | Deep OCT Angiography Image Generation for Motion Artifact Suppression |
| title_fullStr | Deep OCT Angiography Image Generation for Motion Artifact Suppression |
| title_full_unstemmed | Deep OCT Angiography Image Generation for Motion Artifact Suppression |
| title_short | Deep OCT Angiography Image Generation for Motion Artifact Suppression |
| title_sort | deep oct angiography image generation for motion artifact suppression |
| url | https://hdl.handle.net/1721.1/129342 |
| work_keys_str_mv | AT hossbachjulian deepoctangiographyimagegenerationformotionartifactsuppression AT husvogtlennart deepoctangiographyimagegenerationformotionartifactsuppression AT krausmartinf deepoctangiographyimagegenerationformotionartifactsuppression AT fujimotojamesg deepoctangiographyimagegenerationformotionartifactsuppression AT maierandreask deepoctangiographyimagegenerationformotionartifactsuppression |