A Multiscale Attention Feature based Transformer-Residual Combined Network for Retinal Vessel Segmentation.
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| Title: | A Multiscale Attention Feature based Transformer-Residual Combined Network for Retinal Vessel Segmentation. |
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| Authors: | Zhang, Mingwei1, Shi, Lixian1, Zhang, Xiaoyan1, Zhan, Yonghua2, Du, Getao3 gtdu@xupt.edu.cn |
| Source: | Journal of Imaging Science & Technology. Nov/Dec2025, Vol. 69 Issue 6, p1-11. 11p. |
| Subjects: | Retinal imaging, Image segmentation, Computer-assisted image analysis (Medicine), Deep learning, Transformer models, Artificial neural networks, Feature extraction |
| Abstract: | Accurate segmentation and recognition of retinal vessels is a very important medical image analysis technique, which enables clinicians to precisely locate and identify vessels and other tissues in fundus images. However, there are two problems with most existing U-net-based vessel segmentation models. The first is that retinal vessels have very low contrast with the image background, resulting in the loss of much detailed information. The second is that the complex curvature patterns of capillaries result in models that cannot accurately capture the continuity and coherence of the vessels. To solve these two problems, we propose a joint Transformer--Residual network based on a multiscale attention feature (MSAF) mechanism to effectively segment retinal vessels (MATR-Net). In MATR-Net, the convolutional layer in U-net is replaced with a Residual module and a dual encoder branch composed with Transformer to effectively capture the local information and global contextual information of retinal vessels. In addition, an MSAF module is proposed in the encoder part of this paper. By combining features of different scales to obtain more detailed pixels lost due to the pooling layer, the segmentation model effectively improves the feature extraction ability for capillaries with complex curvature patterns and accurately captures the continuity of vessels. To validate the effectiveness of MATR-Net, this study conducts comprehensive experiments on the DRIVE and STARE datasets and compares it with state-of-the-art deep learning models. The results show that MATR-Net exhibits excellent segmentation performance with Dice similarity coefficient and Precision of 84.57%, 80.78%, 84.18%, and 80.99% on DRIVE and STARE, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Imaging Science & Technology is the property of International Society for Imaging Science & Technology 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: 192458520 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Multiscale Attention Feature based Transformer-Residual Combined Network for Retinal Vessel Segmentation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Mingwei%22">Zhang, Mingwei</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Shi%2C+Lixian%22">Shi, Lixian</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaoyan%22">Zhang, Xiaoyan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhan%2C+Yonghua%22">Zhan, Yonghua</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Du%2C+Getao%22">Du, Getao</searchLink><relatesTo>3</relatesTo><i> gtdu@xupt.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Imaging+Science+%26+Technology%22">Journal of Imaging Science & Technology</searchLink>. Nov/Dec2025, Vol. 69 Issue 6, p1-11. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Retinal+imaging%22">Retinal imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+image+analysis+%28Medicine%29%22">Computer-assisted image analysis (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate segmentation and recognition of retinal vessels is a very important medical image analysis technique, which enables clinicians to precisely locate and identify vessels and other tissues in fundus images. However, there are two problems with most existing U-net-based vessel segmentation models. The first is that retinal vessels have very low contrast with the image background, resulting in the loss of much detailed information. The second is that the complex curvature patterns of capillaries result in models that cannot accurately capture the continuity and coherence of the vessels. To solve these two problems, we propose a joint Transformer--Residual network based on a multiscale attention feature (MSAF) mechanism to effectively segment retinal vessels (MATR-Net). In MATR-Net, the convolutional layer in U-net is replaced with a Residual module and a dual encoder branch composed with Transformer to effectively capture the local information and global contextual information of retinal vessels. In addition, an MSAF module is proposed in the encoder part of this paper. By combining features of different scales to obtain more detailed pixels lost due to the pooling layer, the segmentation model effectively improves the feature extraction ability for capillaries with complex curvature patterns and accurately captures the continuity of vessels. To validate the effectiveness of MATR-Net, this study conducts comprehensive experiments on the DRIVE and STARE datasets and compares it with state-of-the-art deep learning models. The results show that MATR-Net exhibits excellent segmentation performance with Dice similarity coefficient and Precision of 84.57%, 80.78%, 84.18%, and 80.99% on DRIVE and STARE, respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Imaging Science & Technology is the property of International Society for Imaging Science & Technology 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: Identifiers: – Type: doi Value: 10.2352/J.ImagingSci.Technol.2025.69.6.060502 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Retinal imaging Type: general – SubjectFull: Image segmentation Type: general – SubjectFull: Computer-assisted image analysis (Medicine) Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Transformer models Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Feature extraction Type: general Titles: – TitleFull: A Multiscale Attention Feature based Transformer-Residual Combined Network for Retinal Vessel Segmentation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Mingwei – PersonEntity: Name: NameFull: Shi, Lixian – PersonEntity: Name: NameFull: Zhang, Xiaoyan – PersonEntity: Name: NameFull: Zhan, Yonghua – PersonEntity: Name: NameFull: Du, Getao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov/Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10623701 Numbering: – Type: volume Value: 69 – Type: issue Value: 6 Titles: – TitleFull: Journal of Imaging Science & Technology Type: main |
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