Improved Segmentation of Infant Retinal Images and Quantitative Vascular Analysis
Retinopathy of Prematurity is a leading cause of visual impairment in preterm infants and is characterized by dilation and tortuosity of the retinal blood vessels in the plus disease stage. However, the diagnosis of plus disease is subjective and qualitative; hence, quantitative methods and computer...
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IEEE
2024-01-01
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| author | Ying Wang Xiaoyu Zheng Chunlei He Jianfeng Zhang Shoujun Huang |
| author_facet | Ying Wang Xiaoyu Zheng Chunlei He Jianfeng Zhang Shoujun Huang |
| author_sort | Ying Wang |
| collection | DOAJ |
| description | Retinopathy of Prematurity is a leading cause of visual impairment in preterm infants and is characterized by dilation and tortuosity of the retinal blood vessels in the plus disease stage. However, the diagnosis of plus disease is subjective and qualitative; hence, quantitative methods and computer-based image analysis are required to improve the objectivity of the diagnosis. In this study, we proposed a computer-based image analysis method aimed at segmenting blood vessels and the optic disc in retinal images and providing quantitative features of the vessels to assist doctors in diagnosing plus disease. This method comprises two main stages. In the first stage, we used fundus images of a preterm infant with manually annotated vessel segmentation labels to train U-Net3 network, which is a U-Net network with the dual attention modules. Simultaneously, we used images with optic disc segmentation labels to train U-Net1 network, which is a U-Net network with half of the channels. The F1 score of the vessel segmentation network was 0.8116, and the sensitivity was 0.8273. The F1 score of the optic disc segmentation network was 0.9346, with a sensitivity of 0.9395. In the second stage, we calculated the vessel tortuosity in different retinal regions of the vessel segmentation images using least-squares linear regression. In addition, we computed the vessel density and width within the optimal region of interest, which is defined as a radius four times the diameter of the optic disc. The quantitative results revealed that the vessel tortuosity, density, and width obtained in this study can be used as diagnostic features. |
| format | Article |
| id | doaj-art-3fcddd1c08c34f73b15f1036e1fe96d3 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-3fcddd1c08c34f73b15f1036e1fe96d32025-08-20T02:38:35ZengIEEEIEEE Access2169-35362024-01-011218184618185710.1109/ACCESS.2024.350944110772091Improved Segmentation of Infant Retinal Images and Quantitative Vascular AnalysisYing Wang0Xiaoyu Zheng1Chunlei He2Jianfeng Zhang3https://orcid.org/0000-0001-8883-3450Shoujun Huang4https://orcid.org/0000-0002-4385-6023School of Mathematical Sciences, Zhejiang Normal University, Jinhua, ChinaDepartment of Ophthalmology, Children’s Hospital, National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou, ChinaCollege of Mathematical Medicine, Zhejiang Normal University, Jinhua, ChinaCollege of Mathematical Medicine, Zhejiang Normal University, Jinhua, ChinaCollege of Mathematical Medicine, Zhejiang Normal University, Jinhua, ChinaRetinopathy of Prematurity is a leading cause of visual impairment in preterm infants and is characterized by dilation and tortuosity of the retinal blood vessels in the plus disease stage. However, the diagnosis of plus disease is subjective and qualitative; hence, quantitative methods and computer-based image analysis are required to improve the objectivity of the diagnosis. In this study, we proposed a computer-based image analysis method aimed at segmenting blood vessels and the optic disc in retinal images and providing quantitative features of the vessels to assist doctors in diagnosing plus disease. This method comprises two main stages. In the first stage, we used fundus images of a preterm infant with manually annotated vessel segmentation labels to train U-Net3 network, which is a U-Net network with the dual attention modules. Simultaneously, we used images with optic disc segmentation labels to train U-Net1 network, which is a U-Net network with half of the channels. The F1 score of the vessel segmentation network was 0.8116, and the sensitivity was 0.8273. The F1 score of the optic disc segmentation network was 0.9346, with a sensitivity of 0.9395. In the second stage, we calculated the vessel tortuosity in different retinal regions of the vessel segmentation images using least-squares linear regression. In addition, we computed the vessel density and width within the optimal region of interest, which is defined as a radius four times the diameter of the optic disc. The quantitative results revealed that the vessel tortuosity, density, and width obtained in this study can be used as diagnostic features.https://ieeexplore.ieee.org/document/10772091/Deep convolutional neural networksplus diseaseretinopathy of prematuritysegmentation |
| spellingShingle | Ying Wang Xiaoyu Zheng Chunlei He Jianfeng Zhang Shoujun Huang Improved Segmentation of Infant Retinal Images and Quantitative Vascular Analysis IEEE Access Deep convolutional neural networks plus disease retinopathy of prematurity segmentation |
| title | Improved Segmentation of Infant Retinal Images and Quantitative Vascular Analysis |
| title_full | Improved Segmentation of Infant Retinal Images and Quantitative Vascular Analysis |
| title_fullStr | Improved Segmentation of Infant Retinal Images and Quantitative Vascular Analysis |
| title_full_unstemmed | Improved Segmentation of Infant Retinal Images and Quantitative Vascular Analysis |
| title_short | Improved Segmentation of Infant Retinal Images and Quantitative Vascular Analysis |
| title_sort | improved segmentation of infant retinal images and quantitative vascular analysis |
| topic | Deep convolutional neural networks plus disease retinopathy of prematurity segmentation |
| url | https://ieeexplore.ieee.org/document/10772091/ |
| work_keys_str_mv | AT yingwang improvedsegmentationofinfantretinalimagesandquantitativevascularanalysis AT xiaoyuzheng improvedsegmentationofinfantretinalimagesandquantitativevascularanalysis AT chunleihe improvedsegmentationofinfantretinalimagesandquantitativevascularanalysis AT jianfengzhang improvedsegmentationofinfantretinalimagesandquantitativevascularanalysis AT shoujunhuang improvedsegmentationofinfantretinalimagesandquantitativevascularanalysis |