A fundus vessel segmentation method based on double skip connections combined with deep supervision

Extensive experiments on publicly available multimodal fundus datasets such as DRIVE, CHASE_DB1, and ROSE-1 demonstrate that the DS2TUNet model attains F1-scores of 0.8195, 0.8362, and 0.8425, with Accuracy of 0.9664, 0.9741, and 0.9557, Sensitivity of 0.8071, 0.8101, and 0.8586, and Specificity of 0.9823, 0.9869, and 0.9713, respectively. Additionally, the model also exhibits excellent test performance on the clinical fundus dataset CSC, with F1-score of 0.7757, Accuracy of 0.9688, Sensitivity of 0.8141, and Specificity of 0.9801 based on the weight trained on the CHASE_DB1 dataset. These results comprehensively validate that the proposed method obtains good performance in fundus vessel segmentation, thereby aiding clinicians in the further diagnosis and treatment of fundus diseases in terms of effectiveness and feasibility.

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Publication Details

Journal
Frontiers in Cell and Developmental Biology
Published
2024-10-03
DOI
https://doi.org/10.3389/fcell.2024.1477819
Citations
1
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.40

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article

A fundus vessel segmentation method based on double skip connections combined with deep supervision

Xiangzhong Xu, Fen Zhou, Zhipeng Yan, Qingyou Liu et al.
1 citations
Frontiers in Cell and Developmental Biology
Retinal Imaging and Analysis
0.40
article

A fundus vessel segmentation method based on double skip connections combined with deep supervision

Xiangzhong Xu, Fen Zhou, Zhipeng Yan, Qingyou Liu, Cheng Wan, Jianguo Xu, Jianxin Shen, Jin Yao
article en
1 citations

Abstract

Extensive experiments on publicly available multimodal fundus datasets such as DRIVE, CHASE_DB1, and ROSE-1 demonstrate that the DS2TUNet model attains F1-scores of 0.8195, 0.8362, and 0.8425, with Accuracy of 0.9664, 0.9741, and 0.9557, Sensitivity of 0.8071, 0.8101, and 0.8586, and Specificity of 0.9823, 0.9869, and 0.9713, respectively. Additionally, the model also exhibits excellent test performance on the clinical fundus dataset CSC, with F1-score of 0.7757, Accuracy of 0.9688, Sensitivity of 0.8141, and Specificity of 0.9801 based on the weight trained on the CHASE_DB1 dataset. These results comprehensively validate that the proposed method obtains good performance in fundus vessel segmentation, thereby aiding clinicians in the further diagnosis and treatment of fundus diseases in terms of effectiveness and feasibility.

Frontiers in Cell and Developmental BiologyVol. 12
Second Affiliated Hospital of Nanjing Medical University (CN), Nanjing University of Aeronautics and Astronautics (CN)
National Natural Science Foundation of China, Nanjing University of Aeronautics and Astronautics, China Scholarship Council, Nanjing Medical University, Nanjing University, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 34%
Retinal Imaging and Analysis
0.40
DOIOpen Access PDFOpenAlex
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A fundus vessel segmentation method based on double skip connections combined with deep supervision — Xiangzhong Xu, Fen Zhou, et al. · Frontiers in Cell and Developmental Biology (2024) | TGRS Research Map | TGRS