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Kreitner, L.* ; Paetzold, J.C. ; Rauch, N.* ; Chen, C.* ; Hagag, A.M.* ; Fayed, A.E.* ; Sivaprasad, S.* ; Rausch, S.* ; Weichsel, J.* ; Menze, B.H.* ; Harders, M.* ; Knier, B.* ; Rueckert, D.* ; Menten, M.J.*

Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations.

IEEE Trans. Med. Imaging 43, 2061-2073 (2024)
Verlagsversion DOI
Open Access Hybrid
Creative Commons Lizenzvertrag
Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Blood vessels; deep learning; image segmentation; OCTA; transfer learning; Images
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Zeitschrift IEEE Transactions on Medical Imaging
Quellenangaben Band: 43, Heft: 6, Seiten: 2061-2073 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort New York, NY [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Tissue Engineering and Regenerative Medicine (ITERM)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505800-001
Förderungen Deutsche Forschungsgemeinschaft DFG
DOI 10.1109/TMI.2024.3354408
WOS ID 001243728100027
Erfassungsdatum 2024-07-09
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