Cargando…

Automated Retinal Layer Segmentation Using Spectral Domain Optical Coherence Tomography: Evaluation of Inter-Session Repeatability and Agreement between Devices

Retinal and intra-retinal layer thicknesses are routinely generated from optical coherence tomography (OCT) images, but on-board software capabilities and image scaling assumptions are not consistent across devices. This study evaluates the device-independent Iowa Reference Algorithms (Iowa Institut...

Descripción completa

Detalles Bibliográficos
Autores principales: Terry, Louise, Cassels, Nicola, Lu, Kelly, Acton, Jennifer H., Margrain, Tom H., North, Rachel V., Fergusson, James, White, Nick, Wood, Ashley
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5010216/
https://www.ncbi.nlm.nih.gov/pubmed/27588683
http://dx.doi.org/10.1371/journal.pone.0162001
_version_ 1782451650241232896
author Terry, Louise
Cassels, Nicola
Lu, Kelly
Acton, Jennifer H.
Margrain, Tom H.
North, Rachel V.
Fergusson, James
White, Nick
Wood, Ashley
author_facet Terry, Louise
Cassels, Nicola
Lu, Kelly
Acton, Jennifer H.
Margrain, Tom H.
North, Rachel V.
Fergusson, James
White, Nick
Wood, Ashley
author_sort Terry, Louise
collection PubMed
description Retinal and intra-retinal layer thicknesses are routinely generated from optical coherence tomography (OCT) images, but on-board software capabilities and image scaling assumptions are not consistent across devices. This study evaluates the device-independent Iowa Reference Algorithms (Iowa Institute for Biomedical Imaging) for automated intra-retinal layer segmentation and image scaling for three OCT systems. Healthy participants (n = 25) underwent macular volume scans using a Cirrus HD-OCT (Zeiss), 3D-OCT 1000 (Topcon), and a non-commercial long-wavelength (1040nm) OCT on two occasions. Mean thickness of 10 intra-retinal layers was measured in three ETDRS subfields (fovea, inner ring and outer ring) using the Iowa Reference Algorithms. Where available, total retinal thicknesses were measured using on-board software. Measured axial eye length (AEL)-dependent scaling was used throughout, with a comparison made to the system-specific fixed-AEL scaling. Inter-session repeatability and agreement between OCT systems and segmentation methods was assessed. Inter-session coefficient of repeatability (CoR) for the foveal subfield total retinal thickness was 3.43μm, 4.76μm, and 5.98μm for the Zeiss, Topcon, and long-wavelength images respectively. For the commercial software, CoR was 4.63μm (Zeiss) and 7.63μm (Topcon). The Iowa Reference Algorithms demonstrated higher repeatability than the on-board software and, in addition, reliably segmented all 10 intra-retinal layers. With fixed-AEL scaling, the algorithm produced significantly different thickness values for the three OCT devices (P<0.05), with these discrepancies generally characterized by an overall offset (bias) and correlations with axial eye length for the foveal subfield and outer ring (P<0.05). This correlation was reduced to an insignificant level in all cases when AEL-dependent scaling was used. Overall, the Iowa Reference Algorithms are viable for clinical and research use in healthy eyes imaged with these devices, however ocular biometry is required for accurate quantification of OCT images.
format Online
Article
Text
id pubmed-5010216
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-50102162016-09-27 Automated Retinal Layer Segmentation Using Spectral Domain Optical Coherence Tomography: Evaluation of Inter-Session Repeatability and Agreement between Devices Terry, Louise Cassels, Nicola Lu, Kelly Acton, Jennifer H. Margrain, Tom H. North, Rachel V. Fergusson, James White, Nick Wood, Ashley PLoS One Research Article Retinal and intra-retinal layer thicknesses are routinely generated from optical coherence tomography (OCT) images, but on-board software capabilities and image scaling assumptions are not consistent across devices. This study evaluates the device-independent Iowa Reference Algorithms (Iowa Institute for Biomedical Imaging) for automated intra-retinal layer segmentation and image scaling for three OCT systems. Healthy participants (n = 25) underwent macular volume scans using a Cirrus HD-OCT (Zeiss), 3D-OCT 1000 (Topcon), and a non-commercial long-wavelength (1040nm) OCT on two occasions. Mean thickness of 10 intra-retinal layers was measured in three ETDRS subfields (fovea, inner ring and outer ring) using the Iowa Reference Algorithms. Where available, total retinal thicknesses were measured using on-board software. Measured axial eye length (AEL)-dependent scaling was used throughout, with a comparison made to the system-specific fixed-AEL scaling. Inter-session repeatability and agreement between OCT systems and segmentation methods was assessed. Inter-session coefficient of repeatability (CoR) for the foveal subfield total retinal thickness was 3.43μm, 4.76μm, and 5.98μm for the Zeiss, Topcon, and long-wavelength images respectively. For the commercial software, CoR was 4.63μm (Zeiss) and 7.63μm (Topcon). The Iowa Reference Algorithms demonstrated higher repeatability than the on-board software and, in addition, reliably segmented all 10 intra-retinal layers. With fixed-AEL scaling, the algorithm produced significantly different thickness values for the three OCT devices (P<0.05), with these discrepancies generally characterized by an overall offset (bias) and correlations with axial eye length for the foveal subfield and outer ring (P<0.05). This correlation was reduced to an insignificant level in all cases when AEL-dependent scaling was used. Overall, the Iowa Reference Algorithms are viable for clinical and research use in healthy eyes imaged with these devices, however ocular biometry is required for accurate quantification of OCT images. Public Library of Science 2016-09-02 /pmc/articles/PMC5010216/ /pubmed/27588683 http://dx.doi.org/10.1371/journal.pone.0162001 Text en © 2016 Terry et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Terry, Louise
Cassels, Nicola
Lu, Kelly
Acton, Jennifer H.
Margrain, Tom H.
North, Rachel V.
Fergusson, James
White, Nick
Wood, Ashley
Automated Retinal Layer Segmentation Using Spectral Domain Optical Coherence Tomography: Evaluation of Inter-Session Repeatability and Agreement between Devices
title Automated Retinal Layer Segmentation Using Spectral Domain Optical Coherence Tomography: Evaluation of Inter-Session Repeatability and Agreement between Devices
title_full Automated Retinal Layer Segmentation Using Spectral Domain Optical Coherence Tomography: Evaluation of Inter-Session Repeatability and Agreement between Devices
title_fullStr Automated Retinal Layer Segmentation Using Spectral Domain Optical Coherence Tomography: Evaluation of Inter-Session Repeatability and Agreement between Devices
title_full_unstemmed Automated Retinal Layer Segmentation Using Spectral Domain Optical Coherence Tomography: Evaluation of Inter-Session Repeatability and Agreement between Devices
title_short Automated Retinal Layer Segmentation Using Spectral Domain Optical Coherence Tomography: Evaluation of Inter-Session Repeatability and Agreement between Devices
title_sort automated retinal layer segmentation using spectral domain optical coherence tomography: evaluation of inter-session repeatability and agreement between devices
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5010216/
https://www.ncbi.nlm.nih.gov/pubmed/27588683
http://dx.doi.org/10.1371/journal.pone.0162001
work_keys_str_mv AT terrylouise automatedretinallayersegmentationusingspectraldomainopticalcoherencetomographyevaluationofintersessionrepeatabilityandagreementbetweendevices
AT casselsnicola automatedretinallayersegmentationusingspectraldomainopticalcoherencetomographyevaluationofintersessionrepeatabilityandagreementbetweendevices
AT lukelly automatedretinallayersegmentationusingspectraldomainopticalcoherencetomographyevaluationofintersessionrepeatabilityandagreementbetweendevices
AT actonjenniferh automatedretinallayersegmentationusingspectraldomainopticalcoherencetomographyevaluationofintersessionrepeatabilityandagreementbetweendevices
AT margraintomh automatedretinallayersegmentationusingspectraldomainopticalcoherencetomographyevaluationofintersessionrepeatabilityandagreementbetweendevices
AT northrachelv automatedretinallayersegmentationusingspectraldomainopticalcoherencetomographyevaluationofintersessionrepeatabilityandagreementbetweendevices
AT fergussonjames automatedretinallayersegmentationusingspectraldomainopticalcoherencetomographyevaluationofintersessionrepeatabilityandagreementbetweendevices
AT whitenick automatedretinallayersegmentationusingspectraldomainopticalcoherencetomographyevaluationofintersessionrepeatabilityandagreementbetweendevices
AT woodashley automatedretinallayersegmentationusingspectraldomainopticalcoherencetomographyevaluationofintersessionrepeatabilityandagreementbetweendevices