Generative adversarial DacFormer network for MRI brain tumor segmentation
Abstract Current brain tumor segmentation methods often utilize a U-Net architecture based on efficient convolutional neural networks. While effective, these architectures primarily model local dependencies, lacking the ability to capture global interactions like pure Transformer. However, using pur...
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| Format: | Article |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02714-4 |
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| author | Muqing Zhang Qiule Sun Yutong Han Mingli Zhang Wei Wang Jianxin Zhang |
| author_facet | Muqing Zhang Qiule Sun Yutong Han Mingli Zhang Wei Wang Jianxin Zhang |
| author_sort | Muqing Zhang |
| collection | DOAJ |
| description | Abstract Current brain tumor segmentation methods often utilize a U-Net architecture based on efficient convolutional neural networks. While effective, these architectures primarily model local dependencies, lacking the ability to capture global interactions like pure Transformer. However, using pure Transformer directly causes the network to lose local feature information. To address this limitation, we propose the Generative Adversarial Dilated Attention Convolutional Transformer(GDacFormer). GDacFormer enhances interactions between tumor regions while balancing global and local information through the integration of adversarial learning with an improved transformer module. Specifically, GDacFormer leverages a generative adversarial segmentation network to learn richer and more detailed features. It integrates a novel Transformer module, DacFormer, featuring multi-scale dilated attention and a next convolution block. This module, embedded within the generator, aggregates semantic multi-scale information, efficiently reduces the redundancy in the self-attention mechanism, and enhances local feature representations, thus refining the brain tumor segmentation results. GDacFormer achieves Dice values for whole tumor, core tumor, and enhancing tumor segmentation of 90.9%/90.8%/93.7%, 84.6%/85.7%/93.5%, and 77.9%/79.3%/86.3% on BraTS2019-2021 datasets. Extensive evaluations demonstrate the effectiveness and competitiveness of GDacFormer. The code for GDacFormer will be made publicly available at https://github.com/MuqinZ/GDacFormer. |
| format | Article |
| id | doaj-art-e6351290a3f3406dbc3d3698b7db8a24 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-e6351290a3f3406dbc3d3698b7db8a242025-08-20T02:34:02ZengNature PortfolioScientific Reports2045-23222025-05-0115111210.1038/s41598-025-02714-4Generative adversarial DacFormer network for MRI brain tumor segmentationMuqing Zhang0Qiule Sun1Yutong Han2Mingli Zhang3Wei Wang4Jianxin Zhang5School of Computer Science and Engineering, Dalian Minzu UniversitySchool of Computer Science and Technology, Dalian University of TechnologySchool of Computer Science and Engineering, Dalian Minzu UniversityMontreal Neurological InstituteSchool of Computer Science and Engineering, Dalian Minzu UniversitySchool of Computer Science and Engineering, Dalian Minzu UniversityAbstract Current brain tumor segmentation methods often utilize a U-Net architecture based on efficient convolutional neural networks. While effective, these architectures primarily model local dependencies, lacking the ability to capture global interactions like pure Transformer. However, using pure Transformer directly causes the network to lose local feature information. To address this limitation, we propose the Generative Adversarial Dilated Attention Convolutional Transformer(GDacFormer). GDacFormer enhances interactions between tumor regions while balancing global and local information through the integration of adversarial learning with an improved transformer module. Specifically, GDacFormer leverages a generative adversarial segmentation network to learn richer and more detailed features. It integrates a novel Transformer module, DacFormer, featuring multi-scale dilated attention and a next convolution block. This module, embedded within the generator, aggregates semantic multi-scale information, efficiently reduces the redundancy in the self-attention mechanism, and enhances local feature representations, thus refining the brain tumor segmentation results. GDacFormer achieves Dice values for whole tumor, core tumor, and enhancing tumor segmentation of 90.9%/90.8%/93.7%, 84.6%/85.7%/93.5%, and 77.9%/79.3%/86.3% on BraTS2019-2021 datasets. Extensive evaluations demonstrate the effectiveness and competitiveness of GDacFormer. The code for GDacFormer will be made publicly available at https://github.com/MuqinZ/GDacFormer.https://doi.org/10.1038/s41598-025-02714-4Brain tumor segmentationU-NetGenerative adversarialTransformer |
| spellingShingle | Muqing Zhang Qiule Sun Yutong Han Mingli Zhang Wei Wang Jianxin Zhang Generative adversarial DacFormer network for MRI brain tumor segmentation Scientific Reports Brain tumor segmentation U-Net Generative adversarial Transformer |
| title | Generative adversarial DacFormer network for MRI brain tumor segmentation |
| title_full | Generative adversarial DacFormer network for MRI brain tumor segmentation |
| title_fullStr | Generative adversarial DacFormer network for MRI brain tumor segmentation |
| title_full_unstemmed | Generative adversarial DacFormer network for MRI brain tumor segmentation |
| title_short | Generative adversarial DacFormer network for MRI brain tumor segmentation |
| title_sort | generative adversarial dacformer network for mri brain tumor segmentation |
| topic | Brain tumor segmentation U-Net Generative adversarial Transformer |
| url | https://doi.org/10.1038/s41598-025-02714-4 |
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