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Impact of manual correction over automated segmentation of spectral domain optical coherence tomography

OBJECTIVE: To study the automated segmentation of retinal layers using spectral domain optical coherence tomography (OCT) and the impact of manual correction over segmentation mistakes. METHODS: This was a retrospective, cross-sectional, comparative study that compared the automated segmentation of...

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Autores principales: de Azevedo, Alexandre Gomes Bortoloti, Takitani, Guilherme Eiichi da Silva, Godoy, Bruno Rebello, Marianelli, Bruna Ferraço, Saraiva, Vinicius, Tavares, Ivan Maynart, Roisman, Luiz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7020356/
https://www.ncbi.nlm.nih.gov/pubmed/32082615
http://dx.doi.org/10.1186/s40942-020-0207-6
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author de Azevedo, Alexandre Gomes Bortoloti
Takitani, Guilherme Eiichi da Silva
Godoy, Bruno Rebello
Marianelli, Bruna Ferraço
Saraiva, Vinicius
Tavares, Ivan Maynart
Roisman, Luiz
author_facet de Azevedo, Alexandre Gomes Bortoloti
Takitani, Guilherme Eiichi da Silva
Godoy, Bruno Rebello
Marianelli, Bruna Ferraço
Saraiva, Vinicius
Tavares, Ivan Maynart
Roisman, Luiz
author_sort de Azevedo, Alexandre Gomes Bortoloti
collection PubMed
description OBJECTIVE: To study the automated segmentation of retinal layers using spectral domain optical coherence tomography (OCT) and the impact of manual correction over segmentation mistakes. METHODS: This was a retrospective, cross-sectional, comparative study that compared the automated segmentation of macular thickness using Spectralis™ OCT technology (Heidelberg Engineering, Heidelberg, Germany) versus manual segmentation in eyes with no macular changes, macular cystoid edema (CME), and choroidal neovascularization (CNV). Automated segmentation of macular thickness was manually corrected by two independent examiners and reanalyzed by them together in case of disagreement. RESULTS: In total, 306 eyes of 254 consecutive patients were evaluated. No statistically significant differences were noted between automated and manual macular thickness measurements in patients with normal maculas, while a statistically significant difference was found in central thickness in patients with CNV and with CME. Segmentation mistakes in macular OCTs were present in 5.3% (5 of 95) in the normal macula group, 16.4% (23 of 140) in the CME group, and 66.2% (47 of 71) in CNV group. The difference between automated and manual macular thickness was higher than 10% in 1.4% (2 of 140) in the CME group and in 28.17% (20 of 71) in the CNV group. Only one case in the normal group had a higher than 10% segmentation error (1 of 95). CONCLUSION: The evaluation of automated segmented OCT images revealed appropriate delimitation of macular thickness in patients with no macular changes or with CME, since the frequency and magnitude of the segmentation mistakes had low impact over clinical evaluation of the images. Conversely, automated macular thickness segmentation in patients with CNV showed a high frequency and magnitude of mistakes, with potential impact on clinical analysis.
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spelling pubmed-70203562020-02-20 Impact of manual correction over automated segmentation of spectral domain optical coherence tomography de Azevedo, Alexandre Gomes Bortoloti Takitani, Guilherme Eiichi da Silva Godoy, Bruno Rebello Marianelli, Bruna Ferraço Saraiva, Vinicius Tavares, Ivan Maynart Roisman, Luiz Int J Retina Vitreous Original Article OBJECTIVE: To study the automated segmentation of retinal layers using spectral domain optical coherence tomography (OCT) and the impact of manual correction over segmentation mistakes. METHODS: This was a retrospective, cross-sectional, comparative study that compared the automated segmentation of macular thickness using Spectralis™ OCT technology (Heidelberg Engineering, Heidelberg, Germany) versus manual segmentation in eyes with no macular changes, macular cystoid edema (CME), and choroidal neovascularization (CNV). Automated segmentation of macular thickness was manually corrected by two independent examiners and reanalyzed by them together in case of disagreement. RESULTS: In total, 306 eyes of 254 consecutive patients were evaluated. No statistically significant differences were noted between automated and manual macular thickness measurements in patients with normal maculas, while a statistically significant difference was found in central thickness in patients with CNV and with CME. Segmentation mistakes in macular OCTs were present in 5.3% (5 of 95) in the normal macula group, 16.4% (23 of 140) in the CME group, and 66.2% (47 of 71) in CNV group. The difference between automated and manual macular thickness was higher than 10% in 1.4% (2 of 140) in the CME group and in 28.17% (20 of 71) in the CNV group. Only one case in the normal group had a higher than 10% segmentation error (1 of 95). CONCLUSION: The evaluation of automated segmented OCT images revealed appropriate delimitation of macular thickness in patients with no macular changes or with CME, since the frequency and magnitude of the segmentation mistakes had low impact over clinical evaluation of the images. Conversely, automated macular thickness segmentation in patients with CNV showed a high frequency and magnitude of mistakes, with potential impact on clinical analysis. BioMed Central 2020-02-14 /pmc/articles/PMC7020356/ /pubmed/32082615 http://dx.doi.org/10.1186/s40942-020-0207-6 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Original Article
de Azevedo, Alexandre Gomes Bortoloti
Takitani, Guilherme Eiichi da Silva
Godoy, Bruno Rebello
Marianelli, Bruna Ferraço
Saraiva, Vinicius
Tavares, Ivan Maynart
Roisman, Luiz
Impact of manual correction over automated segmentation of spectral domain optical coherence tomography
title Impact of manual correction over automated segmentation of spectral domain optical coherence tomography
title_full Impact of manual correction over automated segmentation of spectral domain optical coherence tomography
title_fullStr Impact of manual correction over automated segmentation of spectral domain optical coherence tomography
title_full_unstemmed Impact of manual correction over automated segmentation of spectral domain optical coherence tomography
title_short Impact of manual correction over automated segmentation of spectral domain optical coherence tomography
title_sort impact of manual correction over automated segmentation of spectral domain optical coherence tomography
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7020356/
https://www.ncbi.nlm.nih.gov/pubmed/32082615
http://dx.doi.org/10.1186/s40942-020-0207-6
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