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Eckardt, F.* ; Mittas, R. ; Horlava, N. ; Schiefelbein, J.* ; Asani, B.* ; Michalakis, S.* ; Gerhardt, M.* ; Priglinger, C.* ; Keeser, D.* ; Koutsouleris, N.* ; Priglinger, S.* ; Theis, F.J. ; Peng, T. ; Schworm, B.*

Deep Learning based retinal layer segmentation in optical coherence tomography scans of patients with inherited retinal diseases.

Klin. Monatsbl. Augenheilkd., DOI: 10.1055/a-2227-3742 (2023)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Background: On optical coherence tomography (OCT) scans of patients with inherited retinal diseases (IRDs), the outer nuclear layer (ONL) thickness measurement has been well established as a surrogate marker for photoreceptor preservation. Current automatic segmentation tools fail in OCT segmentation in IRDs, and manual segmentation is time consuming. Methods and Material: Patients with IRD and the availability of an OCT scan were screened for the present study. Additionally, OCT scans of patients without retinal disease were included, to provide training data for the artificial intelligence (AI). We trained a U-net based model on healthy patients and applied a domain adaption technique to IRD patients' scans. Results: We established an AI-based image segmentation algorithm that reliably segments the ONL in OCT scans of IRD patients. In a test dataset, the dice-score of the algorithm was 98.7%. Furthermore, we generated thickness maps of the full retinal thickness and the ONL layer for each patient. Conclusion: Accurate segmentation of anatomical layers on OCT scans plays a crucial role for predictive models linking retinal structure to visual function. The here-presented OCT image segmentation algorithm could provide the basis for further studies on IRDs.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 0023-2165
e-ISSN 1439-3999
Zeitschrift Klinische Monatsblätter für Augenheilkunde
Verlag Thieme
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)