MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation
IntroductionMedical image segmentation is essential for analyzing medical data, improving diagnostics, treatment planning, and research. However, current methods struggle with different imaging types, poor generalization, and rare structure detection.MethodsTo address these issues, we propose MedFus...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1557449/full |
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| author | Jianfei Sun |
| author_facet | Jianfei Sun |
| author_sort | Jianfei Sun |
| collection | DOAJ |
| description | IntroductionMedical image segmentation is essential for analyzing medical data, improving diagnostics, treatment planning, and research. However, current methods struggle with different imaging types, poor generalization, and rare structure detection.MethodsTo address these issues, we propose MedFusion-TransNet, a novel multi-modal fusion approach utilizing transformer-based architectures. By integrating multi-scale feature encoding, attention mechanisms, and dynamic optimization, our method significantly enhances segmentation precision. Our method uses the Context-Aware Segmentation Network (CASNet) and Dynamic Region-Guided Optimization (DRGO) to enhance segmentation by focusing on key anatomical areas.ResultsThese innovations tackle challenges like imbalanced datasets, boundary delineation, and multi-modal complexity. Validation on benchmark datasets demonstrates substantial improvements in accuracy, robustness, and boundary precision, marking a significant step forward in segmentation technologies.DiscussionMedFusion-TransNet offers a transformative tool for advancing the quality and reliability of medical image analysis across diverse clinical applications. |
| format | Article |
| id | doaj-art-4ded1a6881d042d4adfc39f35546bd06 |
| institution | DOAJ |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-4ded1a6881d042d4adfc39f35546bd062025-08-20T03:11:03ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-05-011210.3389/fmed.2025.15574491557449MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentationJianfei SunIntroductionMedical image segmentation is essential for analyzing medical data, improving diagnostics, treatment planning, and research. However, current methods struggle with different imaging types, poor generalization, and rare structure detection.MethodsTo address these issues, we propose MedFusion-TransNet, a novel multi-modal fusion approach utilizing transformer-based architectures. By integrating multi-scale feature encoding, attention mechanisms, and dynamic optimization, our method significantly enhances segmentation precision. Our method uses the Context-Aware Segmentation Network (CASNet) and Dynamic Region-Guided Optimization (DRGO) to enhance segmentation by focusing on key anatomical areas.ResultsThese innovations tackle challenges like imbalanced datasets, boundary delineation, and multi-modal complexity. Validation on benchmark datasets demonstrates substantial improvements in accuracy, robustness, and boundary precision, marking a significant step forward in segmentation technologies.DiscussionMedFusion-TransNet offers a transformative tool for advancing the quality and reliability of medical image analysis across diverse clinical applications.https://www.frontiersin.org/articles/10.3389/fmed.2025.1557449/fullmedical image segmentationmulti-modal fusiontransformer architecturedynamic optimizationboundary precision |
| spellingShingle | Jianfei Sun MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation Frontiers in Medicine medical image segmentation multi-modal fusion transformer architecture dynamic optimization boundary precision |
| title | MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation |
| title_full | MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation |
| title_fullStr | MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation |
| title_full_unstemmed | MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation |
| title_short | MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation |
| title_sort | medfusion transnet multi modal fusion via transformer for enhanced medical image segmentation |
| topic | medical image segmentation multi-modal fusion transformer architecture dynamic optimization boundary precision |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1557449/full |
| work_keys_str_mv | AT jianfeisun medfusiontransnetmultimodalfusionviatransformerforenhancedmedicalimagesegmentation |