FLA-UNet: feature-location attention U-Net for foveal avascular zone segmentation in OCTA images
IntroductionSince optical coherence tomography angiography (OCTA) is non-invasive and non-contact, it is widely used in the study of retinal disease detection. As a key indicator for retinal disease detection, accurate segmentation of foveal avascular zone (FAZ) has an important impact on clinical a...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1463233/full |
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| author | Wei Li Li Cao He Deng |
| author_facet | Wei Li Li Cao He Deng |
| author_sort | Wei Li |
| collection | DOAJ |
| description | IntroductionSince optical coherence tomography angiography (OCTA) is non-invasive and non-contact, it is widely used in the study of retinal disease detection. As a key indicator for retinal disease detection, accurate segmentation of foveal avascular zone (FAZ) has an important impact on clinical application. Although the U-Net and its existing improvement methods have achieved good performance on FAZ segmentation, their generalization ability and segmentation accuracy can be further improved by exploring more effective improvement strategies.MethodsWe propose a novel improved method named Feature-location Attention U-Net (FLA-UNet) by introducing new designed feature-location attention blocks (FLABs) into U-Net and using a joint loss function. The FLAB consists of feature-aware blocks and location-aware blocks in parallel, and is embed into each decoder of U-Net to integrate more marginal information of FAZ and strengthen the connection between target region and boundary information. The joint loss function is composed of the cross-entropy loss (CE loss) function and the Dice coefficient loss (Dice loss) function, and by adjusting the weights of them, the performance of the network on boundary and internal segmentation can be comprehensively considered to improve its accuracy and robustness for FAZ segmentation.ResultsThe qualitative and quantitative comparative experiments on the three datasets of OCTAGON, FAZID and OCTA-500 show that, our proposed FLA-UNet achieves better segmentation quality, and is superior to other existing state-of-the-art methods in terms of the MIoU, ACC and Dice coefficient.DiscussionThe proposed FLA-UNet can effectively improve the accuracy and robustness of FAZ segmentation in OCTA images by introducing feature-location attention blocks into U-Net and using a joint loss function. This has laid a solid theoretical foundation for its application in auxiliary diagnosis of fundus diseases. |
| format | Article |
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| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-ea110575cc0545708b64850e9525baa42025-08-20T03:50:53ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-07-01810.3389/frai.2025.14632331463233FLA-UNet: feature-location attention U-Net for foveal avascular zone segmentation in OCTA imagesWei Li0Li Cao1He Deng2School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, ChinaSchool of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, ChinaSchool of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, ChinaIntroductionSince optical coherence tomography angiography (OCTA) is non-invasive and non-contact, it is widely used in the study of retinal disease detection. As a key indicator for retinal disease detection, accurate segmentation of foveal avascular zone (FAZ) has an important impact on clinical application. Although the U-Net and its existing improvement methods have achieved good performance on FAZ segmentation, their generalization ability and segmentation accuracy can be further improved by exploring more effective improvement strategies.MethodsWe propose a novel improved method named Feature-location Attention U-Net (FLA-UNet) by introducing new designed feature-location attention blocks (FLABs) into U-Net and using a joint loss function. The FLAB consists of feature-aware blocks and location-aware blocks in parallel, and is embed into each decoder of U-Net to integrate more marginal information of FAZ and strengthen the connection between target region and boundary information. The joint loss function is composed of the cross-entropy loss (CE loss) function and the Dice coefficient loss (Dice loss) function, and by adjusting the weights of them, the performance of the network on boundary and internal segmentation can be comprehensively considered to improve its accuracy and robustness for FAZ segmentation.ResultsThe qualitative and quantitative comparative experiments on the three datasets of OCTAGON, FAZID and OCTA-500 show that, our proposed FLA-UNet achieves better segmentation quality, and is superior to other existing state-of-the-art methods in terms of the MIoU, ACC and Dice coefficient.DiscussionThe proposed FLA-UNet can effectively improve the accuracy and robustness of FAZ segmentation in OCTA images by introducing feature-location attention blocks into U-Net and using a joint loss function. This has laid a solid theoretical foundation for its application in auxiliary diagnosis of fundus diseases.https://www.frontiersin.org/articles/10.3389/frai.2025.1463233/fulloptical coherence tomography angiography (OCTA)foveal avascular zone (FAZ) segmentationfeature-location attentionjoint loss functionU-Net |
| spellingShingle | Wei Li Li Cao He Deng FLA-UNet: feature-location attention U-Net for foveal avascular zone segmentation in OCTA images Frontiers in Artificial Intelligence optical coherence tomography angiography (OCTA) foveal avascular zone (FAZ) segmentation feature-location attention joint loss function U-Net |
| title | FLA-UNet: feature-location attention U-Net for foveal avascular zone segmentation in OCTA images |
| title_full | FLA-UNet: feature-location attention U-Net for foveal avascular zone segmentation in OCTA images |
| title_fullStr | FLA-UNet: feature-location attention U-Net for foveal avascular zone segmentation in OCTA images |
| title_full_unstemmed | FLA-UNet: feature-location attention U-Net for foveal avascular zone segmentation in OCTA images |
| title_short | FLA-UNet: feature-location attention U-Net for foveal avascular zone segmentation in OCTA images |
| title_sort | fla unet feature location attention u net for foveal avascular zone segmentation in octa images |
| topic | optical coherence tomography angiography (OCTA) foveal avascular zone (FAZ) segmentation feature-location attention joint loss function U-Net |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1463233/full |
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