Retinal vascular segmentation network based on dual-scale morphological enhancement
Abstract Retinal vessel segmentation is crucial for diagnosing ocular diseases, but current methods struggle with fine vessels and complex structures. This paper proposes a full-resolution network based on dual-scale morphological enhancement (TED-SCNet) to improve the accuracy and robustness of ret...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00191-3 |
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| _version_ | 1849225880100929536 |
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| author | Yunfeng Ni Pei Wang Wei Chen Jie Qi |
| author_facet | Yunfeng Ni Pei Wang Wei Chen Jie Qi |
| author_sort | Yunfeng Ni |
| collection | DOAJ |
| description | Abstract Retinal vessel segmentation is crucial for diagnosing ocular diseases, but current methods struggle with fine vessels and complex structures. This paper proposes a full-resolution network based on dual-scale morphological enhancement (TED-SCNet) to improve the accuracy and robustness of retinal blood vessel segmentation. Initially, a dual-scale shape enhancement module was designed and integrated into the segmentation network to achieve end-to-end contrast enhancement, highlighting fine vascular structures. Subsequently, a feature aggregation module based on dynamic snake-shaped convolutions is introduced, which adapts convolution paths to achieve the fusion of features at different levels. Following this, a deep semantic enhancement module is devised to maximize the enhancement of semantic information. Finally, an over-segmentation-guided classification network is constructed to reduce the misclassification of avascular pixels. This study employs a combination of binary cross-entropy and structural similarity loss functions to accurately generate binary segmentation results. In the DRIVE, CHASE_DB1, and STARE datasets, TED-SCNet achieves AUCs of 98.90%, 99.02%, and 99.16%, respectively, outperforming traditional methods by 0.66%, 0.21%, and 0.32%. The F1 scores for the respective categories are 84.3%, 80.49%, and 84.45%, revealing a considerable progression in the fine vessel segmentation process. The combined loss function enhances the Dice similarity coefficient of thin tubular structures by a margin of 1.23%. In essence, TED-SCNet demonstrates high precision and robustness in retinal vessel segmentation, particularly excelling in fine vessel and complex structure segmentation while maintaining thin tubular structure connectivity. |
| format | Article |
| id | doaj-art-ee6e2a009ed04ee9a1f9adc9b4ef2d83 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-ee6e2a009ed04ee9a1f9adc9b4ef2d832025-08-24T11:53:31ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-08-0137711610.1007/s44443-025-00191-3Retinal vascular segmentation network based on dual-scale morphological enhancementYunfeng Ni0Pei Wang1Wei Chen2Jie Qi3School of Communication and Information Engineering, Xi’an University of Science and TechnologySchool of Communication and Information Engineering, Xi’an University of Science and TechnologySchool of Communication and Information Engineering, Xi’an University of Science and TechnologyOrthopedic Department, Shaanxi Provincial People’s HospitalAbstract Retinal vessel segmentation is crucial for diagnosing ocular diseases, but current methods struggle with fine vessels and complex structures. This paper proposes a full-resolution network based on dual-scale morphological enhancement (TED-SCNet) to improve the accuracy and robustness of retinal blood vessel segmentation. Initially, a dual-scale shape enhancement module was designed and integrated into the segmentation network to achieve end-to-end contrast enhancement, highlighting fine vascular structures. Subsequently, a feature aggregation module based on dynamic snake-shaped convolutions is introduced, which adapts convolution paths to achieve the fusion of features at different levels. Following this, a deep semantic enhancement module is devised to maximize the enhancement of semantic information. Finally, an over-segmentation-guided classification network is constructed to reduce the misclassification of avascular pixels. This study employs a combination of binary cross-entropy and structural similarity loss functions to accurately generate binary segmentation results. In the DRIVE, CHASE_DB1, and STARE datasets, TED-SCNet achieves AUCs of 98.90%, 99.02%, and 99.16%, respectively, outperforming traditional methods by 0.66%, 0.21%, and 0.32%. The F1 scores for the respective categories are 84.3%, 80.49%, and 84.45%, revealing a considerable progression in the fine vessel segmentation process. The combined loss function enhances the Dice similarity coefficient of thin tubular structures by a margin of 1.23%. In essence, TED-SCNet demonstrates high precision and robustness in retinal vessel segmentation, particularly excelling in fine vessel and complex structure segmentation while maintaining thin tubular structure connectivity.https://doi.org/10.1007/s44443-025-00191-3Vessel segmentationDual-scale morphological enhancementOver-segmentation guided classificationVascular connectivity |
| spellingShingle | Yunfeng Ni Pei Wang Wei Chen Jie Qi Retinal vascular segmentation network based on dual-scale morphological enhancement Journal of King Saud University: Computer and Information Sciences Vessel segmentation Dual-scale morphological enhancement Over-segmentation guided classification Vascular connectivity |
| title | Retinal vascular segmentation network based on dual-scale morphological enhancement |
| title_full | Retinal vascular segmentation network based on dual-scale morphological enhancement |
| title_fullStr | Retinal vascular segmentation network based on dual-scale morphological enhancement |
| title_full_unstemmed | Retinal vascular segmentation network based on dual-scale morphological enhancement |
| title_short | Retinal vascular segmentation network based on dual-scale morphological enhancement |
| title_sort | retinal vascular segmentation network based on dual scale morphological enhancement |
| topic | Vessel segmentation Dual-scale morphological enhancement Over-segmentation guided classification Vascular connectivity |
| url | https://doi.org/10.1007/s44443-025-00191-3 |
| work_keys_str_mv | AT yunfengni retinalvascularsegmentationnetworkbasedondualscalemorphologicalenhancement AT peiwang retinalvascularsegmentationnetworkbasedondualscalemorphologicalenhancement AT weichen retinalvascularsegmentationnetworkbasedondualscalemorphologicalenhancement AT jieqi retinalvascularsegmentationnetworkbasedondualscalemorphologicalenhancement |