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Kreitner, L.* ; Ezhov, I.* ; Rueckert, D.* ; Paetzold, J.C. ; Menten, M.J.*

Automated Analysis of Diabetic Retinopathy Using Vessel Segmentation Maps as Inductive Bias.

In: (MIDOG 2022, DRAC 2022: Mitosis Domain Generalization and Diabetic Retinopathy Analysis). Berlin [u.a.]: Springer, 2023. 16-25 (Lect. Notes Comput. Sc. ; 13597 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex. In this work, we investigate a novel method for automated DR grading based on ultra-wide optical coherence tomography angiography (UW-OCTA) images. Our work combines OCTA scans with their vessel segmentations, which then serve as inputs to task specific networks for lesion segmentation, image quality assessment and DR grading. For this, we generate synthetic OCTA images to train a segmentation network that can be directly applied on real OCTA data. We test our approach on MICCAI 2022’s DR analysis challenge (DRAC). In our experiments, the proposed method performs equally well as the baseline model.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Classification ; Diabetic Retinopathy ; Eye ; Miccai Challenges ; Octa ; Segmentation ; Synthetic Data
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel MIDOG 2022, DRAC 2022: Mitosis Domain Generalization and Diabetic Retinopathy Analysis
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 13597 LNCS, Heft: , Seiten: 16-25 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Tissue Engineering and Regenerative Medicine (ITERM)