Dynamic Attention-Guided Convolutional Network for Retinal Vessel Segmentation.
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| Title: | Dynamic Attention-Guided Convolutional Network for Retinal Vessel Segmentation. |
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| Authors: | Zhang, Xihong1 zhangxihong2025@163.com |
| Source: | IAENG International Journal of Computer Science. Jul2026, Vol. 53 Issue 7, p2546-2557. 12p. |
| Subjects: | Image segmentation, Convolutional neural networks, Fundus oculi, Feature extraction, Image processing, Artificial neural networks |
| Abstract: | Precise retinal vessel segmentation is a fundamental task in automated fundus image analysis and computeraided diagnosis for various ocular and systemic diseases. Despite the widespread adoption of U-Net-based architectures, accurate vascular delineation remains challenging due to the intricate morphology of thin vessels, low contrast against heterogeneous backgrounds, and inherent imaging noise. These factors often lead to discontinuous predictions and the omission of delicate capillary branches. To address these challenges, we propose DynamicMSAF-Net, a convolutional framework that integrates a Multi-Scale Attention Fusion (MSAF) module and a Layered Multi-Attention Fusion (LMFA) module. Specifically, MSAF adaptively aggregates vascular cues across diverse receptive fields to enhance multi-scale representation learning, while LMFA refines encoder-decoder feature integration through layered attention to reduce redundancy and preserve structural continuity. Experimental results on the DRIVE and CHASE DB1 benchmarks show that DynamicMSAF-Net achieves strong segmentation performance, yielding AUCs of 98.78% and 99.04%, accuracies of 97.04% and 97.60%, and sensitivities of 82.69% and 85.72%, respectively. The results indicate that the proposed method effectively preserves thinvessel topology and maintains high segmentation fidelity under diverse imaging conditions while remaining computationally efficient. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195088885 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dynamic Attention-Guided Convolutional Network for Retinal Vessel Segmentation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Xihong%22">Zhang, Xihong</searchLink><relatesTo>1</relatesTo><i> zhangxihong2025@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jul2026, Vol. 53 Issue 7, p2546-2557. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Fundus+oculi%22">Fundus oculi</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Precise retinal vessel segmentation is a fundamental task in automated fundus image analysis and computeraided diagnosis for various ocular and systemic diseases. Despite the widespread adoption of U-Net-based architectures, accurate vascular delineation remains challenging due to the intricate morphology of thin vessels, low contrast against heterogeneous backgrounds, and inherent imaging noise. These factors often lead to discontinuous predictions and the omission of delicate capillary branches. To address these challenges, we propose DynamicMSAF-Net, a convolutional framework that integrates a Multi-Scale Attention Fusion (MSAF) module and a Layered Multi-Attention Fusion (LMFA) module. Specifically, MSAF adaptively aggregates vascular cues across diverse receptive fields to enhance multi-scale representation learning, while LMFA refines encoder-decoder feature integration through layered attention to reduce redundancy and preserve structural continuity. Experimental results on the DRIVE and CHASE DB1 benchmarks show that DynamicMSAF-Net achieves strong segmentation performance, yielding AUCs of 98.78% and 99.04%, accuracies of 97.04% and 97.60%, and sensitivities of 82.69% and 85.72%, respectively. The results indicate that the proposed method effectively preserves thinvessel topology and maintains high segmentation fidelity under diverse imaging conditions while remaining computationally efficient. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 2546 Subjects: – SubjectFull: Image segmentation Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Fundus oculi Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Image processing Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: Dynamic Attention-Guided Convolutional Network for Retinal Vessel Segmentation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Xihong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 7 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
| ResultId | 1 |
