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Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations.
IEEE Trans. Med. Imaging 43, 2061-2073 (2024)
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
ISSN (print) / ISBN
0278-0062
e-ISSN
1558-254X
Zeitschrift
IEEE Transactions on Medical Imaging
Quellenangaben
Band: 43,
Heft: 6,
Seiten: 2061-2073
Verlag
Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort
New York, NY [u.a.]
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
Förderungen
Deutsche Forschungsgemeinschaft DFG