Fusing multispectral information for retinal layer segmentation
Abstract Extensive research on retinal layer segmentation (RLS) using deep learning (DL) is mostly approaching a performance plateau, primarily due to reliance on structural information alone. To address the present situation, we conduct the first study on the impact of multi-spectral information (M...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01446-z |
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| author | Xiang He Fuwang Wu Kaixuan Hu Lizhen Cui Weiye Song Yi Wan |
| author_facet | Xiang He Fuwang Wu Kaixuan Hu Lizhen Cui Weiye Song Yi Wan |
| author_sort | Xiang He |
| collection | DOAJ |
| description | Abstract Extensive research on retinal layer segmentation (RLS) using deep learning (DL) is mostly approaching a performance plateau, primarily due to reliance on structural information alone. To address the present situation, we conduct the first study on the impact of multi-spectral information (MSI) on RLS. Our experimental results show that incorporating MSI significantly improves segmentation accuracy for retinal layer optical coherence tomography (OCT) images. Furthermore, we investigate the primary factors influencing MSI, including the number of multi-spectral images, spectral bandwidth, and the different spectral combinations, to assess their impacts on the accuracy of RLS. Building upon this foundation, we have incorporated MSI into RLS methods, yielding exceptional performance in segmentation outcomes, and these findings have been validated in OCT images across both the near-infrared and visible-light spectral ranges. Fusing MSI provides a novel approach to improving RLS accuracy, further demonstrating the importance of open-source MSI information in OCT devices. |
| format | Article |
| id | doaj-art-5905ce44975a43b996c3d41e2ce90ad8 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-5905ce44975a43b996c3d41e2ce90ad82025-01-19T12:39:46ZengNature Portfolionpj Digital Medicine2398-63522025-01-018111510.1038/s41746-025-01446-zFusing multispectral information for retinal layer segmentationXiang He0Fuwang Wu1Kaixuan Hu2Lizhen Cui3Weiye Song4Yi Wan5School of Mechanical Engineering, Shandong UniversitySchool of Mechanical Engineering, Shandong UniversitySchool of Mechanical Engineering, Shandong UniversityJoint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong UniversitySchool of Mechanical Engineering, Shandong UniversitySchool of Mechanical Engineering, Shandong UniversityAbstract Extensive research on retinal layer segmentation (RLS) using deep learning (DL) is mostly approaching a performance plateau, primarily due to reliance on structural information alone. To address the present situation, we conduct the first study on the impact of multi-spectral information (MSI) on RLS. Our experimental results show that incorporating MSI significantly improves segmentation accuracy for retinal layer optical coherence tomography (OCT) images. Furthermore, we investigate the primary factors influencing MSI, including the number of multi-spectral images, spectral bandwidth, and the different spectral combinations, to assess their impacts on the accuracy of RLS. Building upon this foundation, we have incorporated MSI into RLS methods, yielding exceptional performance in segmentation outcomes, and these findings have been validated in OCT images across both the near-infrared and visible-light spectral ranges. Fusing MSI provides a novel approach to improving RLS accuracy, further demonstrating the importance of open-source MSI information in OCT devices.https://doi.org/10.1038/s41746-025-01446-z |
| spellingShingle | Xiang He Fuwang Wu Kaixuan Hu Lizhen Cui Weiye Song Yi Wan Fusing multispectral information for retinal layer segmentation npj Digital Medicine |
| title | Fusing multispectral information for retinal layer segmentation |
| title_full | Fusing multispectral information for retinal layer segmentation |
| title_fullStr | Fusing multispectral information for retinal layer segmentation |
| title_full_unstemmed | Fusing multispectral information for retinal layer segmentation |
| title_short | Fusing multispectral information for retinal layer segmentation |
| title_sort | fusing multispectral information for retinal layer segmentation |
| url | https://doi.org/10.1038/s41746-025-01446-z |
| work_keys_str_mv | AT xianghe fusingmultispectralinformationforretinallayersegmentation AT fuwangwu fusingmultispectralinformationforretinallayersegmentation AT kaixuanhu fusingmultispectralinformationforretinallayersegmentation AT lizhencui fusingmultispectralinformationforretinallayersegmentation AT weiyesong fusingmultispectralinformationforretinallayersegmentation AT yiwan fusingmultispectralinformationforretinallayersegmentation |