Evaluation of retinal structure changes with AI-based OCT image segmentation for sodium iodate induced retinal degeneration
Segmentations of retinal optical coherence tomography (OCT) images provide valuable information about each specific retinal layer. However, processing images from degenerative retina remains challenging. This study developed artificial intelligence (AI)-based segmentation to analyze structure change...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Cellular Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fncel.2025.1605639/full |
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| author | Yong Zeng Jiaming Zhou Jiaming Zhou Yichao Li Bruno Alvisio Jacob Czech David Bissig Haohua Qian |
| author_facet | Yong Zeng Jiaming Zhou Jiaming Zhou Yichao Li Bruno Alvisio Jacob Czech David Bissig Haohua Qian |
| author_sort | Yong Zeng |
| collection | DOAJ |
| description | Segmentations of retinal optical coherence tomography (OCT) images provide valuable information about each specific retinal layer. However, processing images from degenerative retina remains challenging. This study developed artificial intelligence (AI)-based segmentation to analyze structure changes in sodium iodate (SI)-treated mice. The software is capable of segmenting seven retinal layers and one choroid layer. Analyzing OCT images captured at days post SI-injection (PI) revealed early changes in the retinal pigment epithelium (RPE) layer, with increase in thickness and reduction in reflectance calculated by estimated Attenuation Coefficients (eAC). On the other hand, eAC for outer nuclear layer (ONL) exhibited early and sustained increase after SI treatment. SI induced exponential reduction in ONL thickness with a half-reduction time of about 3 days, indicating progressive photoreceptor degeneration. The extent of degeneration was correlated with ONL eAC level at PI1. Inner retinal layers showed bi-phasic reactions, with initial increases in layer thickness that peaked at around PI3, followed by gradual reduction to lower than baseline levels. In addition, SI also induced transient increases in vitreous particles concentrated around the optic nerve head. Furthermore, there was a gradual reduction of choroid thickness after SI treatment. These results indicate the AI-segmentation tool's usefulness for providing a sensitive and accurate assessment of structure changes in diseased retina and revealed more detailed characterization of SI-induced degeneration in all retinal layers with distinct time courses. Our results also support ONL reflectance changes as an early biomarker for retinal degeneration. |
| format | Article |
| id | doaj-art-07469d2dc89041399ebb6998979c64dc |
| institution | Kabale University |
| issn | 1662-5102 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Cellular Neuroscience |
| spelling | doaj-art-07469d2dc89041399ebb6998979c64dc2025-08-20T03:31:11ZengFrontiers Media S.A.Frontiers in Cellular Neuroscience1662-51022025-06-011910.3389/fncel.2025.16056391605639Evaluation of retinal structure changes with AI-based OCT image segmentation for sodium iodate induced retinal degenerationYong Zeng0Jiaming Zhou1Jiaming Zhou2Yichao Li3Bruno Alvisio4Jacob Czech5David Bissig6Haohua Qian7Visual Function Core, National Eye Institute, National Institutes of Health, Bethesda, MD, United StatesVisual Function Core, National Eye Institute, National Institutes of Health, Bethesda, MD, United StatesNorthwestern University, Evanston, IL, United StatesVisual Function Core, National Eye Institute, National Institutes of Health, Bethesda, MD, United StatesOSIO Bioinformatics Core, National Eye Institute, National Institutes of Health, Bethesda, MD, United StatesOSIO Bioinformatics Core, National Eye Institute, National Institutes of Health, Bethesda, MD, United StatesDepartment of Neurology, University of California, Davis, Sacramento, CA, United StatesVisual Function Core, National Eye Institute, National Institutes of Health, Bethesda, MD, United StatesSegmentations of retinal optical coherence tomography (OCT) images provide valuable information about each specific retinal layer. However, processing images from degenerative retina remains challenging. This study developed artificial intelligence (AI)-based segmentation to analyze structure changes in sodium iodate (SI)-treated mice. The software is capable of segmenting seven retinal layers and one choroid layer. Analyzing OCT images captured at days post SI-injection (PI) revealed early changes in the retinal pigment epithelium (RPE) layer, with increase in thickness and reduction in reflectance calculated by estimated Attenuation Coefficients (eAC). On the other hand, eAC for outer nuclear layer (ONL) exhibited early and sustained increase after SI treatment. SI induced exponential reduction in ONL thickness with a half-reduction time of about 3 days, indicating progressive photoreceptor degeneration. The extent of degeneration was correlated with ONL eAC level at PI1. Inner retinal layers showed bi-phasic reactions, with initial increases in layer thickness that peaked at around PI3, followed by gradual reduction to lower than baseline levels. In addition, SI also induced transient increases in vitreous particles concentrated around the optic nerve head. Furthermore, there was a gradual reduction of choroid thickness after SI treatment. These results indicate the AI-segmentation tool's usefulness for providing a sensitive and accurate assessment of structure changes in diseased retina and revealed more detailed characterization of SI-induced degeneration in all retinal layers with distinct time courses. Our results also support ONL reflectance changes as an early biomarker for retinal degeneration.https://www.frontiersin.org/articles/10.3389/fncel.2025.1605639/fullmouseretinaoptical coherence tomographysodium iodatesegmentationchoroid |
| spellingShingle | Yong Zeng Jiaming Zhou Jiaming Zhou Yichao Li Bruno Alvisio Jacob Czech David Bissig Haohua Qian Evaluation of retinal structure changes with AI-based OCT image segmentation for sodium iodate induced retinal degeneration Frontiers in Cellular Neuroscience mouse retina optical coherence tomography sodium iodate segmentation choroid |
| title | Evaluation of retinal structure changes with AI-based OCT image segmentation for sodium iodate induced retinal degeneration |
| title_full | Evaluation of retinal structure changes with AI-based OCT image segmentation for sodium iodate induced retinal degeneration |
| title_fullStr | Evaluation of retinal structure changes with AI-based OCT image segmentation for sodium iodate induced retinal degeneration |
| title_full_unstemmed | Evaluation of retinal structure changes with AI-based OCT image segmentation for sodium iodate induced retinal degeneration |
| title_short | Evaluation of retinal structure changes with AI-based OCT image segmentation for sodium iodate induced retinal degeneration |
| title_sort | evaluation of retinal structure changes with ai based oct image segmentation for sodium iodate induced retinal degeneration |
| topic | mouse retina optical coherence tomography sodium iodate segmentation choroid |
| url | https://www.frontiersin.org/articles/10.3389/fncel.2025.1605639/full |
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