Attention residual network for medical ultrasound image segmentation
Abstract Ultrasound imaging can distinctly display the morphology and structure of internal organs within the human body, enabling the examination of organs like the breast, liver, and thyroid. It can identify the locations of tumors, nodules, and other lesions, thereby serving as an efficacious too...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04086-1 |
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| author | Honghua Liu Peiqin Zhang Jiamin Hu Yini Huang Shanshan Zuo Lu Li Mailan Liu Chang She |
| author_facet | Honghua Liu Peiqin Zhang Jiamin Hu Yini Huang Shanshan Zuo Lu Li Mailan Liu Chang She |
| author_sort | Honghua Liu |
| collection | DOAJ |
| description | Abstract Ultrasound imaging can distinctly display the morphology and structure of internal organs within the human body, enabling the examination of organs like the breast, liver, and thyroid. It can identify the locations of tumors, nodules, and other lesions, thereby serving as an efficacious tool for treatment detection and rehabilitation evaluation. Typically, the attending physician is required to manually demarcate the boundaries of lesion locations, such as tumors, in ultrasound images. Nevertheless, several issues exist. The high noise level in ultrasound images, the degradation of image quality due to the impact of surrounding tissues, and the influence of the operator’s experience and proficiency on the determination of lesion locations can all contribute to a reduction in the accuracy of delineating the boundaries of lesion sites. In the wake of the advancement of deep learning, its application in medical image segmentation is becoming increasingly prevalent. For instance, while the U-Net model has demonstrated a favorable performance in medical image segmentation, the convolution layers of the traditional U-Net model are relatively simplistic, leading to suboptimal extraction of global information. Moreover, due to the significant noise present in ultrasound images, the model is prone to interference. In this research, we propose an Attention Residual Network model (ARU-Net). By incorporating residual connections within the encoder section, the learning capacity of the model is enhanced. Additionally, a spatial hybrid convolution module is integrated to augment the model’s ability to extract global information and deepen the vertical architecture of the network. During the feature fusion stage of the skip connections, a channel attention mechanism and a multi-convolutional self-attention mechanism are respectively introduced to suppress noisy points within the fused feature maps, enabling the model to acquire more information regarding the target region. Finally, the predictive efficacy of the model was evaluated using publicly accessible breast ultrasound and thyroid ultrasound data. The ARU-Net achieved mean Intersection over Union (mIoU) values of 82.59% and 84.88%, accuracy values of 97.53% and 96.09%, and F1-score values of 90.06% and 89.7% for breast and thyroid ultrasound, respectively. |
| format | Article |
| id | doaj-art-ef81a1d43cb3402eae0cc8bcb39912db |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ef81a1d43cb3402eae0cc8bcb39912db2025-08-20T03:45:28ZengNature PortfolioScientific Reports2045-23222025-07-0115111410.1038/s41598-025-04086-1Attention residual network for medical ultrasound image segmentationHonghua Liu0Peiqin Zhang1Jiamin Hu2Yini Huang3Shanshan Zuo4Lu Li5Mailan Liu6Chang She7Hunan University of Chinese MedicineHunan Traditional Chinese Medical CollegeHunan University of Chinese MedicineChangsha Hospital of Traditional Chinese Medicine (Changsha Eighth Hospital)Changsha Hospital of Traditional Chinese Medicine (Changsha Eighth Hospital)Changsha Hospital of Traditional Chinese Medicine (Changsha Eighth Hospital)Hunan University of Chinese MedicineChangsha Hospital of Traditional Chinese Medicine (Changsha Eighth Hospital)Abstract Ultrasound imaging can distinctly display the morphology and structure of internal organs within the human body, enabling the examination of organs like the breast, liver, and thyroid. It can identify the locations of tumors, nodules, and other lesions, thereby serving as an efficacious tool for treatment detection and rehabilitation evaluation. Typically, the attending physician is required to manually demarcate the boundaries of lesion locations, such as tumors, in ultrasound images. Nevertheless, several issues exist. The high noise level in ultrasound images, the degradation of image quality due to the impact of surrounding tissues, and the influence of the operator’s experience and proficiency on the determination of lesion locations can all contribute to a reduction in the accuracy of delineating the boundaries of lesion sites. In the wake of the advancement of deep learning, its application in medical image segmentation is becoming increasingly prevalent. For instance, while the U-Net model has demonstrated a favorable performance in medical image segmentation, the convolution layers of the traditional U-Net model are relatively simplistic, leading to suboptimal extraction of global information. Moreover, due to the significant noise present in ultrasound images, the model is prone to interference. In this research, we propose an Attention Residual Network model (ARU-Net). By incorporating residual connections within the encoder section, the learning capacity of the model is enhanced. Additionally, a spatial hybrid convolution module is integrated to augment the model’s ability to extract global information and deepen the vertical architecture of the network. During the feature fusion stage of the skip connections, a channel attention mechanism and a multi-convolutional self-attention mechanism are respectively introduced to suppress noisy points within the fused feature maps, enabling the model to acquire more information regarding the target region. Finally, the predictive efficacy of the model was evaluated using publicly accessible breast ultrasound and thyroid ultrasound data. The ARU-Net achieved mean Intersection over Union (mIoU) values of 82.59% and 84.88%, accuracy values of 97.53% and 96.09%, and F1-score values of 90.06% and 89.7% for breast and thyroid ultrasound, respectively.https://doi.org/10.1038/s41598-025-04086-1Image segmentationUltrasound imagesAttention mechanismU-NetResidual connection |
| spellingShingle | Honghua Liu Peiqin Zhang Jiamin Hu Yini Huang Shanshan Zuo Lu Li Mailan Liu Chang She Attention residual network for medical ultrasound image segmentation Scientific Reports Image segmentation Ultrasound images Attention mechanism U-Net Residual connection |
| title | Attention residual network for medical ultrasound image segmentation |
| title_full | Attention residual network for medical ultrasound image segmentation |
| title_fullStr | Attention residual network for medical ultrasound image segmentation |
| title_full_unstemmed | Attention residual network for medical ultrasound image segmentation |
| title_short | Attention residual network for medical ultrasound image segmentation |
| title_sort | attention residual network for medical ultrasound image segmentation |
| topic | Image segmentation Ultrasound images Attention mechanism U-Net Residual connection |
| url | https://doi.org/10.1038/s41598-025-04086-1 |
| work_keys_str_mv | AT honghualiu attentionresidualnetworkformedicalultrasoundimagesegmentation AT peiqinzhang attentionresidualnetworkformedicalultrasoundimagesegmentation AT jiaminhu attentionresidualnetworkformedicalultrasoundimagesegmentation AT yinihuang attentionresidualnetworkformedicalultrasoundimagesegmentation AT shanshanzuo attentionresidualnetworkformedicalultrasoundimagesegmentation AT luli attentionresidualnetworkformedicalultrasoundimagesegmentation AT mailanliu attentionresidualnetworkformedicalultrasoundimagesegmentation AT changshe attentionresidualnetworkformedicalultrasoundimagesegmentation |