EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound images
Abstract The accurate segmentation of breast tumours is important for the diagnosis and treatment of breast cancer. When using the classic U‐Net, Attention‐UNet, and UNet++ to segment the tumour, there are problems of oversegmentation, incorrect segmentation, and poor edge continuity. In this paper,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-02-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12966 |
| _version_ | 1797333777112891392 |
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| author | Guizeng You Xinwu Yang Xuanbo Lee Kongqiang Zhu |
| author_facet | Guizeng You Xinwu Yang Xuanbo Lee Kongqiang Zhu |
| author_sort | Guizeng You |
| collection | DOAJ |
| description | Abstract The accurate segmentation of breast tumours is important for the diagnosis and treatment of breast cancer. When using the classic U‐Net, Attention‐UNet, and UNet++ to segment the tumour, there are problems of oversegmentation, incorrect segmentation, and poor edge continuity. In this paper, an effective tumour segmentation method, EfficientUNet, is proposed. EfficientUNet adopts a step‐by‐step enhancement method, combining ResNet18, a channel attention mechanism and deep supervision. ResNet18, as the encoder of the whole network, solves the problem of gradient disappearanceand improves the feature extraction ability of the model. The channel attention module makes the model more accurate in tumour edge processing. The deep supervision technology accelerates the model training and provides the convergence direction for the model. In addition, it is found that when adjusting the size of the image, the method of image filling before clipping (or zooming) is more conducive to model learning than the direct interpolation method. And a comparative experiment wasperformed on dataset B. Compared to U‐Net, Attention‐UNet and UNet++, EfficientUNet has the highest performance. Finally, the ablation experiment also indicated the effectiveness of each module in EfficientUNet. |
| first_indexed | 2024-03-08T08:10:00Z |
| format | Article |
| id | doaj.art-e6218534d718479b9d37124110b94287 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-03-08T08:10:00Z |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-e6218534d718479b9d37124110b942872024-02-02T09:31:01ZengWileyIET Image Processing1751-96591751-96672024-02-0118252353410.1049/ipr2.12966EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound imagesGuizeng You0Xinwu Yang1Xuanbo Lee2Kongqiang Zhu3Facaulty of Informtion and Technology Beijing University of Technology Beijing ChinaFacaulty of Informtion and Technology Beijing University of Technology Beijing ChinaFacaulty of Informtion and Technology Beijing University of Technology Beijing ChinaFacaulty of Informtion and Technology Beijing University of Technology Beijing ChinaAbstract The accurate segmentation of breast tumours is important for the diagnosis and treatment of breast cancer. When using the classic U‐Net, Attention‐UNet, and UNet++ to segment the tumour, there are problems of oversegmentation, incorrect segmentation, and poor edge continuity. In this paper, an effective tumour segmentation method, EfficientUNet, is proposed. EfficientUNet adopts a step‐by‐step enhancement method, combining ResNet18, a channel attention mechanism and deep supervision. ResNet18, as the encoder of the whole network, solves the problem of gradient disappearanceand improves the feature extraction ability of the model. The channel attention module makes the model more accurate in tumour edge processing. The deep supervision technology accelerates the model training and provides the convergence direction for the model. In addition, it is found that when adjusting the size of the image, the method of image filling before clipping (or zooming) is more conducive to model learning than the direct interpolation method. And a comparative experiment wasperformed on dataset B. Compared to U‐Net, Attention‐UNet and UNet++, EfficientUNet has the highest performance. Finally, the ablation experiment also indicated the effectiveness of each module in EfficientUNet.https://doi.org/10.1049/ipr2.12966cancerconvolutional neural networksimage segmentationultrasonic imaging |
| spellingShingle | Guizeng You Xinwu Yang Xuanbo Lee Kongqiang Zhu EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound images IET Image Processing cancer convolutional neural networks image segmentation ultrasonic imaging |
| title | EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound images |
| title_full | EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound images |
| title_fullStr | EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound images |
| title_full_unstemmed | EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound images |
| title_short | EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound images |
| title_sort | efficientunet an efficient solution for breast tumour segmentation in ultrasound images |
| topic | cancer convolutional neural networks image segmentation ultrasonic imaging |
| url | https://doi.org/10.1049/ipr2.12966 |
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