Investigation of Structural Alterations in Inherited Retinal Diseases: A Quantitative SD-OCT-Analysis of Retinal Layer Thicknesses in Light of Underlying Genetic Mutations

Gersch, Julia; Hufendiek, Katerina; Delarocque, Julien; Framme, Carsten; Jacobsen, Christina; Stöhr, Heidi; Kellner, Ulrich; Hufendiek, Karsten

doi:10.3390/ijms232416007

Cited by 4 publications

(4 citation statements)

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“…In a study on patients with RPE65-related IRD by Jacobson and colleagues, 96% of retinal loci with residual light sensitivity had a measurable ONL while in 75% of retinal loci without residual sensitivity, no ONL was measurable [20]. Not only the “OCTExplorer’’ used for this study, but also most other commercially available automated segmentation tools fail to reliably segment the distinct layers in OCT scans of patients with IRDs, which results in time consuming manual correction and/or segmentation [19,21].…”

Section: Discussionmentioning

confidence: 99%

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

Eckardt,

Mittas,

Horlava

et al. 2023

Preprint

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BackgroundOn 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 MaterialPatients 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.ResultsWe 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 was98.7%. Furthermore, we generated thickness maps of the full retinal thickness and the ONL layer for each patient.ConclusionAccurate 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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Section: Discussionmentioning

confidence: 99%

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

Eckardt,

Mittas,

Horlava

et al. 2023

Preprint

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show abstract

“…In a study of patients with RPE65 -related IRD by Jacobson and colleagues, 96% of retinal loci with residual light sensitivity had a measurable ONL while in 75% of retinal loci without residual sensitivity, no ONL was measurable 20 . Not only the “OCTExplorer” used for this study, but also most other commercially available automated segmentation tools, fail to reliably segment the distinct layers in OCT scans of patients with IRDs, which results in time-consuming manual correction and/or segmentation 19 , 21 .…”

Section: Discussionmentioning

confidence: 99%

“…tools fail to reliably segment the distinct layers in OCT scans of patients with IRDs, which results in time consuming manual correction and/or segmentation [19,21].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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

Eckardt,

Mittas,

Horlava

et al. 2023

Klin Monbl Augenheilkd

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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. Hintergrund: Bei der optischen Kohärenztomographie (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, welche Netzhautstruktur und Sehfunktion korrelieren. Der hier vorgestellte OCT-Bildsegmentierungsalgorithmus könnte die Grundlage für weitere Studien bezüglich erblichen Netzhauterkrankungen darstellen.

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“…SD-OCT and en face OCT also provide quantitative measurements of the mean retinal thickness and volume and can be utilized in monitoring disease progression [ 128 , 129 , 130 ]. Unsurprisingly, the mean central foveal thickness and total macular volume were found to decrease over time [ 131 , 132 ], and quantification of the EZ defect and RPE atrophy on serial SD-OCT scans were found to be predictors of progression [ 133 , 134 ].…”

Section: Oct and Octa In Inherited Retinal Degenerationsmentioning

confidence: 99%

Optical Coherence Tomography and Optical Coherence Tomography Angiography in Pediatric Retinal Diseases

et al. 2023

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Indirect ophthalmoscopy and handheld retinal imaging are the most common and traditional modalities for the evaluation and documentation of the pediatric fundus, especially for pre-verbal children. Optical coherence tomography (OCT) allows for in vivo visualization that resembles histology, and optical coherence tomography angiography (OCTA) allows for non-invasive depth-resolved imaging of the retinal vasculature. Both OCT and OCTA were extensively used and studied in adults, but not in children. The advent of prototype handheld OCT and OCTA have allowed for detailed imaging in younger infants and even neonates in the neonatal care intensive unit with retinopathy of prematurity (ROP). In this review, we discuss the use of OCTA and OCTA in various pediatric retinal diseases, including ROP, familial exudative vitreoretinopathy (FEVR), Coats disease and other less common diseases. For example, handheld portable OCT was shown to detect subclinical macular edema and incomplete foveal development in ROP, as well as subretinal exudation and fibrosis in Coats disease. Some challenges in the pediatric age group include the lack of a normative database and the difficulty in image registration for longitudinal comparison. We believe that technological improvements in the use of OCT and OCTA will improve our understanding and care of pediatric retina patients in the future.

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Investigation of Structural Alterations in Inherited Retinal Diseases: A Quantitative SD-OCT-Analysis of Retinal Layer Thicknesses in Light of Underlying Genetic Mutations

Cited by 4 publications

References 73 publications

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

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

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

Optical Coherence Tomography and Optical Coherence Tomography Angiography in Pediatric Retinal Diseases

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