CC BY-NC-ND 4.0 · Klin Monbl Augenheilkd
DOI: 10.1055/a-2227-3742
Klinische Studie

Deep Learning-Based Retinal Layer Segmentation in Optical Coherence Tomography Scans of Patients with Inherited Retinal Diseases

Deep-Learning-basierte Segmentierung von Aufnahmen aus optischer Kohärenztomografie bei Patienten mit erblichen Netzhauterkrankungen
Franziska Eckardt
1   Department of Ophthalmology, LMU University Hospital, LMU Munich, Munich, Germany
,
Robin Mittas
2   Institute for Computational Biology, Helmholtz Munich, Munich, Germany
,
Nastassya Horlava
2   Institute for Computational Biology, Helmholtz Munich, Munich, Germany
,
Johannes Schiefelbein
1   Department of Ophthalmology, LMU University Hospital, LMU Munich, Munich, Germany
,
Ben Asani
1   Department of Ophthalmology, LMU University Hospital, LMU Munich, Munich, Germany
,
Stylianos Michalakis 
1   Department of Ophthalmology, LMU University Hospital, LMU Munich, Munich, Germany
,
Maximilian Gerhardt
1   Department of Ophthalmology, LMU University Hospital, LMU Munich, Munich, Germany
,
Claudia Priglinger 
1   Department of Ophthalmology, LMU University Hospital, LMU Munich, Munich, Germany
,
Daniel Keeser
3   Department of Psychiatry und Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
,
Nikolaos Koutsouleris
3   Department of Psychiatry und Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
,
Siegfried Priglinger
1   Department of Ophthalmology, LMU University Hospital, LMU Munich, Munich, Germany
,
Fabian Theis
2   Institute for Computational Biology, Helmholtz Munich, Munich, Germany
,
Tingying Peng
2   Institute for Computational Biology, Helmholtz Munich, Munich, Germany
,
Benedikt Schworm
1   Department of Ophthalmology, LMU University Hospital, LMU Munich, Munich, Germany
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Abstract

Background In optical coherence tomography (OCT) scans of patients with inherited retinal diseases (IRDs), the measurement of the thickness of the outer nuclear layer (ONL) 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 an available OCT scan were screened for the present study. Additionally, OCT scans of patients without retinal disease were included to provide training data for artificial intelligence (AI). We trained a U-net-based model on healthy patients and applied a domain adaption technique to the IRD patientsʼ 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. Our algorithm for segmentation of OCT images could provide the basis for further studies on IRDs.

Zusammenfassung

Hintergrund Bei der optischen Kohärenztomografie (OCT) von Patienten mit erblichen Netzhauterkrankungen hat sich die Messung der äußeren Körnerschichtdicke (ONL) als Marker für den Erhalt der Photorezeptoren bewährt. Derzeitige automatische Segmentierungsprogramme versagen bei der OCT-Segmentierung dieser Patienten, und die manuelle Segmentierung ist zeitaufwendig.

Methoden und Material Für die vorliegende Studie wurden Patienten mit erblichen Netzhauterkrankungen und der Verfügbarkeit eines OCT-Scans eingeschlossen. Zusätzlich wurden OCT-Scans von Patienten ohne Netzhauterkrankung einbezogen, um Trainingsdaten für die künstliche Intelligenz (KI) zu generieren. Wir trainierten ein auf einem U-Netz basierendes Modell an gesunden Patienten und wendeten eine Anpassungsmethode auf die pathologisch veränderten Scans von Patienten an.

Ergebnisse Es wurde ein KI-basierter Bildsegmentierungsalgorithmus entwickelt, der die ONL in OCT-Scans von Patienten mit erblichen Netzhauterkrankungen zuverlässig segmentieren kann. In einem Testdatensatz lag der Dice-Score des Algorithmus bei 98,7%. Außerdem erstellten wir für jeden Patienten Dickenkarten der gesamten Netzhautdicke und der ONL-Schicht.

Schlussfolgerung Eine präzise Segmentierung anatomischer Schichten auf OCT-Scans ist entscheidend für Prognosemodelle, die Netzhautstruktur und Sehfunktion korrelieren. Der hier vorgestellte OCT-Bildsegmentierungsalgorithmus könnte die Grundlage für weitere Studien bez. erblicher Netzhauterkrankungen darstellen.

Conclusion Box

Already known:

  • AI has an increasingly important role in ophthalmology. To date, most research has focused on the high prevalence ophthalmic diseases.

  • Our study addresses a critical relevant problem, focusing on reliable retinal layer segmentation for IRD patients. Accurate segmentation of anatomical layers in OCT scans plays a key role in the correlation of retinal structure to visual function.

Newly described:

  • We have developed a deep learning algorithm that allows accurate segmentation of pathologically altered OCT scans in patients with IRDs and generates a retinal thickness map.

  • Future work will explore calculating retinal layer thickness and correlating it with functional data, like visual fields. Our aim is to contribute to a greater understanding of disease, and to improve the evaluation of treatment outcomes in the future.

Key words

retina - inherited retinal disease - OCT

Schlüsselwörter

Retina - erbliche Netzhauterkrankungen - OCT


Publication History

Received: 10 October 2023

Accepted: 23 October 2023

Accepted Manuscript online:
12 December 2023

Article published online:
14 February 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commecial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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