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Deep learning-based 3D OCT imaging for detection of lamina cribrosa defects in eyes with high myopia
The lamina cribrosa (LC) is a collagenous tissue located in the optic nerve head, and its dissection is observed in eyes with pathologic myopia as a LC defect (LCD). The diagnosis of LCD has been difficult because the LC is located deep beneath the retinal nerve fibers. The purpose of this study was...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789076/ https://www.ncbi.nlm.nih.gov/pubmed/36564438 http://dx.doi.org/10.1038/s41598-022-26520-4 |
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author | Ota-Itadani, Mako Takahashi, Hiroyuki Mao, Zaixing Igarashi-Yokoi, Tae Yoshida, Takeshi Ohno-Matsui, Kyoko |
author_facet | Ota-Itadani, Mako Takahashi, Hiroyuki Mao, Zaixing Igarashi-Yokoi, Tae Yoshida, Takeshi Ohno-Matsui, Kyoko |
author_sort | Ota-Itadani, Mako |
collection | PubMed |
description | The lamina cribrosa (LC) is a collagenous tissue located in the optic nerve head, and its dissection is observed in eyes with pathologic myopia as a LC defect (LCD). The diagnosis of LCD has been difficult because the LC is located deep beneath the retinal nerve fibers. The purpose of this study was to determine the prevalence and three-dimensional shape of LCDs in highly myopic eyes. Swept-source OCT scan images of a 3 × 3-mm cube centered on the optic disc were obtained from 119 eyes of 62 highly myopic patients. Each LC was manually labelled in cross-sectional OCT images along the axial, coronal, and sagittal planes. A deep convolutional neural network (DCNN) was trained with the manually labeled images, and the trained DCNN was applied to the detection of the LC in every image in each plane. Three-dimensional images of the LC were generated from the labeled image of each eye. The results showed that LCDs were detected in 12 of the 42 (29%) eyes in which an LC was visible. The LCDs ran vertically at the temporal edge of the optic disc. In conclusion, 3D OCT imaging with the application of DCNN is helpful in diagnosing LCDs. |
format | Online Article Text |
id | pubmed-9789076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97890762022-12-25 Deep learning-based 3D OCT imaging for detection of lamina cribrosa defects in eyes with high myopia Ota-Itadani, Mako Takahashi, Hiroyuki Mao, Zaixing Igarashi-Yokoi, Tae Yoshida, Takeshi Ohno-Matsui, Kyoko Sci Rep Article The lamina cribrosa (LC) is a collagenous tissue located in the optic nerve head, and its dissection is observed in eyes with pathologic myopia as a LC defect (LCD). The diagnosis of LCD has been difficult because the LC is located deep beneath the retinal nerve fibers. The purpose of this study was to determine the prevalence and three-dimensional shape of LCDs in highly myopic eyes. Swept-source OCT scan images of a 3 × 3-mm cube centered on the optic disc were obtained from 119 eyes of 62 highly myopic patients. Each LC was manually labelled in cross-sectional OCT images along the axial, coronal, and sagittal planes. A deep convolutional neural network (DCNN) was trained with the manually labeled images, and the trained DCNN was applied to the detection of the LC in every image in each plane. Three-dimensional images of the LC were generated from the labeled image of each eye. The results showed that LCDs were detected in 12 of the 42 (29%) eyes in which an LC was visible. The LCDs ran vertically at the temporal edge of the optic disc. In conclusion, 3D OCT imaging with the application of DCNN is helpful in diagnosing LCDs. Nature Publishing Group UK 2022-12-23 /pmc/articles/PMC9789076/ /pubmed/36564438 http://dx.doi.org/10.1038/s41598-022-26520-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ota-Itadani, Mako Takahashi, Hiroyuki Mao, Zaixing Igarashi-Yokoi, Tae Yoshida, Takeshi Ohno-Matsui, Kyoko Deep learning-based 3D OCT imaging for detection of lamina cribrosa defects in eyes with high myopia |
title | Deep learning-based 3D OCT imaging for detection of lamina cribrosa defects in eyes with high myopia |
title_full | Deep learning-based 3D OCT imaging for detection of lamina cribrosa defects in eyes with high myopia |
title_fullStr | Deep learning-based 3D OCT imaging for detection of lamina cribrosa defects in eyes with high myopia |
title_full_unstemmed | Deep learning-based 3D OCT imaging for detection of lamina cribrosa defects in eyes with high myopia |
title_short | Deep learning-based 3D OCT imaging for detection of lamina cribrosa defects in eyes with high myopia |
title_sort | deep learning-based 3d oct imaging for detection of lamina cribrosa defects in eyes with high myopia |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789076/ https://www.ncbi.nlm.nih.gov/pubmed/36564438 http://dx.doi.org/10.1038/s41598-022-26520-4 |
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