GRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound images
Abstract Ultrasound imaging is widely used for diagnosing various medical conditions. However, lesion segmentation in ultrasound images is challenging due to low contrast, noise, blurred boundaries, and variability in lesion characteristics. To address these issues, we propose a Global Response Doub...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00206-z |
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| _version_ | 1849225856269942784 |
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| author | Xiaokai Jiang Xuewen Ding Jinying Ma Chunyu Liu Xinyi Li |
| author_facet | Xiaokai Jiang Xuewen Ding Jinying Ma Chunyu Liu Xinyi Li |
| author_sort | Xiaokai Jiang |
| collection | DOAJ |
| description | Abstract Ultrasound imaging is widely used for diagnosing various medical conditions. However, lesion segmentation in ultrasound images is challenging due to low contrast, noise, blurred boundaries, and variability in lesion characteristics. To address these issues, we propose a Global Response Double U-shaped Network, a hybrid CNN-Transformer architecture designed for lesion segmentation in ultrasound images. The encoder-decoder backbone is constructed using a U-shaped Dilated Convolution module, which effectively captures fine-grained local features and enhances boundary delineation, particularly under low-contrast conditions. To improve global context modeling, this paper proposes the Global Response Transformer Block in the bottleneck, enabling the network to capture long-range dependencies and structural variability in lesion appearance. By modeling interactions across distant regions, the block more effectively captures the variability in lesion shape, size, and location, enhancing segmentation accuracy for complex and irregular structures in ultrasound images. Furthermore, we design a Multi-Scale Linear Attention Gate to refine skip connections by emphasizing salient features and suppressing redundancy, thereby mitigating noise interference and improving decoding efficiency. By suppressing speckle noise and enhancing critical features, this mechanism improves segmentation accuracy and ensures robustness in complex ultrasound imaging scenarios. The proposed method has been extensively evaluated on publicly available ultrasound image datasets, including breast and thyroid lesion data, demonstrating its effectiveness and robustness in segmenting complex and low-contrast lesions under real-world imaging conditions. |
| format | Article |
| id | doaj-art-9a87668857c6434e94541ba8b83f46d5 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-9a87668857c6434e94541ba8b83f46d52025-08-24T11:53:35ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-08-0137711910.1007/s44443-025-00206-zGRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound imagesXiaokai Jiang0Xuewen Ding1Jinying Ma2Chunyu Liu3Xinyi Li4School of Electronic Engineering, Tianjin University of Technology and EducationSchool of Electronic Engineering, Tianjin University of Technology and EducationSchool of Electronic Engineering, Tianjin University of Technology and EducationSchool of Electronic Engineering, Tianjin University of Technology and EducationSchool of Electronic Engineering, Tianjin University of Technology and EducationAbstract Ultrasound imaging is widely used for diagnosing various medical conditions. However, lesion segmentation in ultrasound images is challenging due to low contrast, noise, blurred boundaries, and variability in lesion characteristics. To address these issues, we propose a Global Response Double U-shaped Network, a hybrid CNN-Transformer architecture designed for lesion segmentation in ultrasound images. The encoder-decoder backbone is constructed using a U-shaped Dilated Convolution module, which effectively captures fine-grained local features and enhances boundary delineation, particularly under low-contrast conditions. To improve global context modeling, this paper proposes the Global Response Transformer Block in the bottleneck, enabling the network to capture long-range dependencies and structural variability in lesion appearance. By modeling interactions across distant regions, the block more effectively captures the variability in lesion shape, size, and location, enhancing segmentation accuracy for complex and irregular structures in ultrasound images. Furthermore, we design a Multi-Scale Linear Attention Gate to refine skip connections by emphasizing salient features and suppressing redundancy, thereby mitigating noise interference and improving decoding efficiency. By suppressing speckle noise and enhancing critical features, this mechanism improves segmentation accuracy and ensures robustness in complex ultrasound imaging scenarios. The proposed method has been extensively evaluated on publicly available ultrasound image datasets, including breast and thyroid lesion data, demonstrating its effectiveness and robustness in segmenting complex and low-contrast lesions under real-world imaging conditions.https://doi.org/10.1007/s44443-025-00206-zUltrasound imagesLesion segmentationU-NetTransformerResponse normalizationAttention mechanism |
| spellingShingle | Xiaokai Jiang Xuewen Ding Jinying Ma Chunyu Liu Xinyi Li GRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound images Journal of King Saud University: Computer and Information Sciences Ultrasound images Lesion segmentation U-Net Transformer Response normalization Attention mechanism |
| title | GRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound images |
| title_full | GRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound images |
| title_fullStr | GRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound images |
| title_full_unstemmed | GRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound images |
| title_short | GRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound images |
| title_sort | gru2 net global response double u shaped network for lesion segmentation in ultrasound images |
| topic | Ultrasound images Lesion segmentation U-Net Transformer Response normalization Attention mechanism |
| url | https://doi.org/10.1007/s44443-025-00206-z |
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