Medical image segmentation by combining feature enhancement Swin Transformer and UperNet
Abstract Medical image segmentation plays a crucial role in assisting clinical diagnosis, yet existing models often struggle with handling diverse and complex medical data, particularly when dealing with multi-scale organ and tissue structures. This paper proposes a novel medical image segmentation...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97779-6 |
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| author | Lin Zhang Xiaochun Yin Xuqi Liu Zengguang Liu |
| author_facet | Lin Zhang Xiaochun Yin Xuqi Liu Zengguang Liu |
| author_sort | Lin Zhang |
| collection | DOAJ |
| description | Abstract Medical image segmentation plays a crucial role in assisting clinical diagnosis, yet existing models often struggle with handling diverse and complex medical data, particularly when dealing with multi-scale organ and tissue structures. This paper proposes a novel medical image segmentation model, FE-SwinUper, designed to address these challenges by integrating the strengths of the Swin Transformer and UPerNet architectures. The objective is to enhance multi-scale feature extraction and improve the fusion of hierarchical organ and tissue representations through a feature enhancement Swin Transformer (FE-ST) backbone and an adaptive feature fusion (AFF) module. The FE-ST backbone utilizes self-attention mechanisms to efficiently extract rich spatial and contextual features across different scales, while the AFF module adapts to multi-scale feature fusion, mitigating the loss of contextual information. We evaluate the model on two publicly available medical image segmentation datasets: Synapse multi-organ segmentation dataset and the ACDC cardiac segmentation dataset. Our results show that FE-SwinUper outperforms existing state-of-the-art models in terms of Dice coefficient, pixel accuracy, and Hausdorff distance. The model achieves a Dice score of 91.58% on the Synapse dataset and 90.15% on the ACDC dataset. These results demonstrate the robustness and efficiency of the proposed model, indicating its potential for real-world clinical applications. |
| format | Article |
| id | doaj-art-f8f264ae3e074c53adba1eedb6d0dc2f |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f8f264ae3e074c53adba1eedb6d0dc2f2025-08-20T03:13:55ZengNature PortfolioScientific Reports2045-23222025-04-0115111310.1038/s41598-025-97779-6Medical image segmentation by combining feature enhancement Swin Transformer and UperNetLin Zhang0Xiaochun Yin1Xuqi Liu2Zengguang Liu3College of Computer Science, Weifang University of Science and TechnologyCollege of Computer Science, Weifang University of Science and TechnologyCollege of Computer Science, Weifang University of Science and TechnologySchool of Information Engineering, Shandong Vocational College of Science and TechnologyAbstract Medical image segmentation plays a crucial role in assisting clinical diagnosis, yet existing models often struggle with handling diverse and complex medical data, particularly when dealing with multi-scale organ and tissue structures. This paper proposes a novel medical image segmentation model, FE-SwinUper, designed to address these challenges by integrating the strengths of the Swin Transformer and UPerNet architectures. The objective is to enhance multi-scale feature extraction and improve the fusion of hierarchical organ and tissue representations through a feature enhancement Swin Transformer (FE-ST) backbone and an adaptive feature fusion (AFF) module. The FE-ST backbone utilizes self-attention mechanisms to efficiently extract rich spatial and contextual features across different scales, while the AFF module adapts to multi-scale feature fusion, mitigating the loss of contextual information. We evaluate the model on two publicly available medical image segmentation datasets: Synapse multi-organ segmentation dataset and the ACDC cardiac segmentation dataset. Our results show that FE-SwinUper outperforms existing state-of-the-art models in terms of Dice coefficient, pixel accuracy, and Hausdorff distance. The model achieves a Dice score of 91.58% on the Synapse dataset and 90.15% on the ACDC dataset. These results demonstrate the robustness and efficiency of the proposed model, indicating its potential for real-world clinical applications.https://doi.org/10.1038/s41598-025-97779-6Medical imageSemantic segmentationDeep learningFeature enhancement |
| spellingShingle | Lin Zhang Xiaochun Yin Xuqi Liu Zengguang Liu Medical image segmentation by combining feature enhancement Swin Transformer and UperNet Scientific Reports Medical image Semantic segmentation Deep learning Feature enhancement |
| title | Medical image segmentation by combining feature enhancement Swin Transformer and UperNet |
| title_full | Medical image segmentation by combining feature enhancement Swin Transformer and UperNet |
| title_fullStr | Medical image segmentation by combining feature enhancement Swin Transformer and UperNet |
| title_full_unstemmed | Medical image segmentation by combining feature enhancement Swin Transformer and UperNet |
| title_short | Medical image segmentation by combining feature enhancement Swin Transformer and UperNet |
| title_sort | medical image segmentation by combining feature enhancement swin transformer and upernet |
| topic | Medical image Semantic segmentation Deep learning Feature enhancement |
| url | https://doi.org/10.1038/s41598-025-97779-6 |
| work_keys_str_mv | AT linzhang medicalimagesegmentationbycombiningfeatureenhancementswintransformerandupernet AT xiaochunyin medicalimagesegmentationbycombiningfeatureenhancementswintransformerandupernet AT xuqiliu medicalimagesegmentationbycombiningfeatureenhancementswintransformerandupernet AT zengguangliu medicalimagesegmentationbycombiningfeatureenhancementswintransformerandupernet |