SGNet: A Structure-Guided Network with Dual-Domain Boundary Enhancement and Semantic Fusion for Skin Lesion Segmentation
Segmentation of skin lesions in dermoscopic images is critical for the accurate diagnosis of skin cancers, particularly malignant melanoma, yet it is hindered by irregular lesion shapes, blurred boundaries, low contrast, and artifacts, such as hair interference. Conventional deep learning methods, t...
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2025-07-01
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| author | Haijiao Yun Qingyu Du Ziqing Han Mingjing Li Le Yang Xinyang Liu Chao Wang Weitian Ma |
| author_facet | Haijiao Yun Qingyu Du Ziqing Han Mingjing Li Le Yang Xinyang Liu Chao Wang Weitian Ma |
| author_sort | Haijiao Yun |
| collection | DOAJ |
| description | Segmentation of skin lesions in dermoscopic images is critical for the accurate diagnosis of skin cancers, particularly malignant melanoma, yet it is hindered by irregular lesion shapes, blurred boundaries, low contrast, and artifacts, such as hair interference. Conventional deep learning methods, typically based on UNet or Transformer architectures, often face limitations in regard to fully exploiting lesion features and incur high computational costs, compromising precise lesion delineation. To overcome these challenges, we propose SGNet, a structure-guided network, integrating a hybrid CNN–Mamba framework for robust skin lesion segmentation. The SGNet employs the Visual Mamba (VMamba) encoder to efficiently extract multi-scale features, followed by the Dual-Domain Boundary Enhancer (DDBE), which refines boundary representations and suppresses noise through spatial and frequency-domain processing. The Semantic-Texture Fusion Unit (STFU) adaptively integrates low-level texture with high-level semantic features, while the Structure-Aware Guidance Module (SAGM) generates coarse segmentation maps to provide global structural guidance. The Guided Multi-Scale Refiner (GMSR) further optimizes boundary details through a multi-scale semantic attention mechanism. Comprehensive experiments based on the ISIC2017, ISIC2018, and PH2 datasets demonstrate SGNet’s superior performance, with average improvements of 3.30% in terms of the mean Intersection over Union (mIoU) value and 1.77% in regard to the Dice Similarity Coefficient (DSC) compared to state-of-the-art methods. Ablation studies confirm the effectiveness of each component, highlighting SGNet’s exceptional accuracy and robust generalization for computer-aided dermatological diagnosis. |
| format | Article |
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| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Sensors |
| spelling | doaj-art-e697dc01879d4d0e9d435e0eaea718592025-08-20T04:00:54ZengMDPI AGSensors1424-82202025-07-012515465210.3390/s25154652SGNet: A Structure-Guided Network with Dual-Domain Boundary Enhancement and Semantic Fusion for Skin Lesion SegmentationHaijiao Yun0Qingyu Du1Ziqing Han2Mingjing Li3Le Yang4Xinyang Liu5Chao Wang6Weitian Ma7School of Electronic Information Engineering, Changchun University, Changchun 130022, ChinaSchool of Electronic Information Engineering, Changchun University, Changchun 130022, ChinaSchool of Electronic Information Engineering, Changchun University, Changchun 130022, ChinaSchool of Electronic Information Engineering, Changchun University, Changchun 130022, ChinaSchool of Electronic Information Engineering, Changchun University, Changchun 130022, ChinaSchool of Electronic Information Engineering, Changchun University, Changchun 130022, ChinaSchool of Electronic Information Engineering, Changchun University, Changchun 130022, ChinaSchool of Electronic Information Engineering, Changchun University, Changchun 130022, ChinaSegmentation of skin lesions in dermoscopic images is critical for the accurate diagnosis of skin cancers, particularly malignant melanoma, yet it is hindered by irregular lesion shapes, blurred boundaries, low contrast, and artifacts, such as hair interference. Conventional deep learning methods, typically based on UNet or Transformer architectures, often face limitations in regard to fully exploiting lesion features and incur high computational costs, compromising precise lesion delineation. To overcome these challenges, we propose SGNet, a structure-guided network, integrating a hybrid CNN–Mamba framework for robust skin lesion segmentation. The SGNet employs the Visual Mamba (VMamba) encoder to efficiently extract multi-scale features, followed by the Dual-Domain Boundary Enhancer (DDBE), which refines boundary representations and suppresses noise through spatial and frequency-domain processing. The Semantic-Texture Fusion Unit (STFU) adaptively integrates low-level texture with high-level semantic features, while the Structure-Aware Guidance Module (SAGM) generates coarse segmentation maps to provide global structural guidance. The Guided Multi-Scale Refiner (GMSR) further optimizes boundary details through a multi-scale semantic attention mechanism. Comprehensive experiments based on the ISIC2017, ISIC2018, and PH2 datasets demonstrate SGNet’s superior performance, with average improvements of 3.30% in terms of the mean Intersection over Union (mIoU) value and 1.77% in regard to the Dice Similarity Coefficient (DSC) compared to state-of-the-art methods. Ablation studies confirm the effectiveness of each component, highlighting SGNet’s exceptional accuracy and robust generalization for computer-aided dermatological diagnosis.https://www.mdpi.com/1424-8220/25/15/4652computer-aided diagnosisstructure guidancefeature fusionVisual Mamba |
| spellingShingle | Haijiao Yun Qingyu Du Ziqing Han Mingjing Li Le Yang Xinyang Liu Chao Wang Weitian Ma SGNet: A Structure-Guided Network with Dual-Domain Boundary Enhancement and Semantic Fusion for Skin Lesion Segmentation Sensors computer-aided diagnosis structure guidance feature fusion Visual Mamba |
| title | SGNet: A Structure-Guided Network with Dual-Domain Boundary Enhancement and Semantic Fusion for Skin Lesion Segmentation |
| title_full | SGNet: A Structure-Guided Network with Dual-Domain Boundary Enhancement and Semantic Fusion for Skin Lesion Segmentation |
| title_fullStr | SGNet: A Structure-Guided Network with Dual-Domain Boundary Enhancement and Semantic Fusion for Skin Lesion Segmentation |
| title_full_unstemmed | SGNet: A Structure-Guided Network with Dual-Domain Boundary Enhancement and Semantic Fusion for Skin Lesion Segmentation |
| title_short | SGNet: A Structure-Guided Network with Dual-Domain Boundary Enhancement and Semantic Fusion for Skin Lesion Segmentation |
| title_sort | sgnet a structure guided network with dual domain boundary enhancement and semantic fusion for skin lesion segmentation |
| topic | computer-aided diagnosis structure guidance feature fusion Visual Mamba |
| url | https://www.mdpi.com/1424-8220/25/15/4652 |
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