Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images
Abstract The structure of the human vitreous varies considerably because of age-related liquefactions of the vitreous gel. These changes are poorly studied in vivo mainly because their high transparency and mobility make it difficult to obtain reliable and repeatable images of the vitreous. Optical...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-17615-z |
| _version_ | 1818496976336977920 |
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| author | Hiroyuki Takahashi Zaixing Mao Ran Du Kyoko Ohno-Matsui |
| author_facet | Hiroyuki Takahashi Zaixing Mao Ran Du Kyoko Ohno-Matsui |
| author_sort | Hiroyuki Takahashi |
| collection | DOAJ |
| description | Abstract The structure of the human vitreous varies considerably because of age-related liquefactions of the vitreous gel. These changes are poorly studied in vivo mainly because their high transparency and mobility make it difficult to obtain reliable and repeatable images of the vitreous. Optical coherence tomography can detect the boundaries between the vitreous gel and vitreous fluid, but it is difficult to obtain high resolution images that can be used to convert the images to three-dimensional (3D) images. Thus, the purpose of this study was to determine the shape and characteristics of the vitreous fluid using machine learning-based 3D modeling in which manually labelled fluid areas were used to train deep convolutional neural network (DCNN). The trained DCNN labelled vitreous fluid automatically and allowed us to obtain 3D vitreous model and to quantify the vitreous fluidic cavities. The mean volume and surface area of posterior vitreous fluidic cavities are 19.6 ± 7.8 mm3 and 104.0 ± 18.9 mm2 in eyes of 17 school children. The results suggested that vitreous fluidic cavities expanded as the cavities connects with each other, and this modeling system provided novel imaging markers for aging and eye diseases. |
| first_indexed | 2024-12-10T18:39:19Z |
| format | Article |
| id | doaj.art-9815796ca6884f578ca1df8299f310f6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-10T18:39:19Z |
| publishDate | 2022-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-9815796ca6884f578ca1df8299f310f62022-12-22T01:37:42ZengNature PortfolioScientific Reports2045-23222022-08-011211810.1038/s41598-022-17615-zMachine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic imagesHiroyuki Takahashi0Zaixing Mao1Ran Du2Kyoko Ohno-Matsui3Department of Ophthalmology and Visual Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU)R&D Division, Topcon CorporationDepartment of Ophthalmology and Visual Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU)Department of Ophthalmology and Visual Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU)Abstract The structure of the human vitreous varies considerably because of age-related liquefactions of the vitreous gel. These changes are poorly studied in vivo mainly because their high transparency and mobility make it difficult to obtain reliable and repeatable images of the vitreous. Optical coherence tomography can detect the boundaries between the vitreous gel and vitreous fluid, but it is difficult to obtain high resolution images that can be used to convert the images to three-dimensional (3D) images. Thus, the purpose of this study was to determine the shape and characteristics of the vitreous fluid using machine learning-based 3D modeling in which manually labelled fluid areas were used to train deep convolutional neural network (DCNN). The trained DCNN labelled vitreous fluid automatically and allowed us to obtain 3D vitreous model and to quantify the vitreous fluidic cavities. The mean volume and surface area of posterior vitreous fluidic cavities are 19.6 ± 7.8 mm3 and 104.0 ± 18.9 mm2 in eyes of 17 school children. The results suggested that vitreous fluidic cavities expanded as the cavities connects with each other, and this modeling system provided novel imaging markers for aging and eye diseases.https://doi.org/10.1038/s41598-022-17615-z |
| spellingShingle | Hiroyuki Takahashi Zaixing Mao Ran Du Kyoko Ohno-Matsui Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images Scientific Reports |
| title | Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images |
| title_full | Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images |
| title_fullStr | Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images |
| title_full_unstemmed | Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images |
| title_short | Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images |
| title_sort | machine learning based 3d modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images |
| url | https://doi.org/10.1038/s41598-022-17615-z |
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