UJAT-Net: A U-Net Combined Joint-Attention and Transformer for Breast Tubule Segmentation in H&E Stained Images
The formation of breast tubules is an important evaluation index in the pathological grading of breast cancer. However, the tubules of breast present a wide variety of morphologies and a significant demonstrated significant advantages in the automatic analysis of histopathology images. We propose a...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10445190/ |
| _version_ | 1827308189013508096 |
|---|---|
| author | Li-Wang Liu Zhao Huang Kao-Yan Lu Zu-Xuan Wang Yao-Ming Liang Shi-Yu Lin Yan-Hong Ji |
| author_facet | Li-Wang Liu Zhao Huang Kao-Yan Lu Zu-Xuan Wang Yao-Ming Liang Shi-Yu Lin Yan-Hong Ji |
| author_sort | Li-Wang Liu |
| collection | DOAJ |
| description | The formation of breast tubules is an important evaluation index in the pathological grading of breast cancer. However, the tubules of breast present a wide variety of morphologies and a significant demonstrated significant advantages in the automatic analysis of histopathology images. We propose a Joint Attention and Transformer U-Net network to accurately segment breast tubules, named UJAT-Net. UJAT-Net uses the Joint Attention Block (named JA BLOCK) as the encoder of the network to enhance the extraction effect of the network for different layer features. And the Channel Cross fusion with Transformer (named CCT) module is used as the skip connection structure of the network. Furthermore, we employ a Transpose Cross Attention (named TCA) module as the decoder of the network to fuse the features of the skip connection layer and the decoder. Experimental results on our own invasive breast cancer tubule (Tubule of Breast Cancer, TBC) dataset and the benchmark dataset (Glas) of the GlaS challenge contest at MICCAI’2015 demonstrate that our method achieves competitive performance. |
| first_indexed | 2024-04-24T18:54:10Z |
| format | Article |
| id | doaj.art-9e1adff096524bb48689166fa0e7f5de |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:54:10Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9e1adff096524bb48689166fa0e7f5de2024-03-26T17:46:41ZengIEEEIEEE Access2169-35362024-01-0112345823459110.1109/ACCESS.2024.336967810445190UJAT-Net: A U-Net Combined Joint-Attention and Transformer for Breast Tubule Segmentation in H&E Stained ImagesLi-Wang Liu0Zhao Huang1https://orcid.org/0009-0008-1102-5744Kao-Yan Lu2Zu-Xuan Wang3Yao-Ming Liang4Shi-Yu Lin5https://orcid.org/0009-0007-7529-5906Yan-Hong Ji6https://orcid.org/0000-0002-7570-1480Key Laboratory of Atomic and Subatomic Structure and Quantum Control (Ministry of Education), Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, School of Physics, South China Normal University, Guangzhou, ChinaKey Laboratory of Atomic and Subatomic Structure and Quantum Control (Ministry of Education), Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, School of Physics, South China Normal University, Guangzhou, ChinaKey Laboratory of Atomic and Subatomic Structure and Quantum Control (Ministry of Education), Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, School of Physics, South China Normal University, Guangzhou, ChinaKey Laboratory of Atomic and Subatomic Structure and Quantum Control (Ministry of Education), Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, School of Physics, South China Normal University, Guangzhou, ChinaKey Laboratory of Atomic and Subatomic Structure and Quantum Control (Ministry of Education), Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, School of Physics, South China Normal University, Guangzhou, ChinaKey Laboratory of Atomic and Subatomic Structure and Quantum Control (Ministry of Education), Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, School of Physics, South China Normal University, Guangzhou, ChinaKey Laboratory of Atomic and Subatomic Structure and Quantum Control (Ministry of Education), Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, School of Physics, South China Normal University, Guangzhou, ChinaThe formation of breast tubules is an important evaluation index in the pathological grading of breast cancer. However, the tubules of breast present a wide variety of morphologies and a significant demonstrated significant advantages in the automatic analysis of histopathology images. We propose a Joint Attention and Transformer U-Net network to accurately segment breast tubules, named UJAT-Net. UJAT-Net uses the Joint Attention Block (named JA BLOCK) as the encoder of the network to enhance the extraction effect of the network for different layer features. And the Channel Cross fusion with Transformer (named CCT) module is used as the skip connection structure of the network. Furthermore, we employ a Transpose Cross Attention (named TCA) module as the decoder of the network to fuse the features of the skip connection layer and the decoder. Experimental results on our own invasive breast cancer tubule (Tubule of Breast Cancer, TBC) dataset and the benchmark dataset (Glas) of the GlaS challenge contest at MICCAI’2015 demonstrate that our method achieves competitive performance.https://ieeexplore.ieee.org/document/10445190/Hematoxylin-eosin (H&E) stained imagestubule segmentationU-Netattention mechanism |
| spellingShingle | Li-Wang Liu Zhao Huang Kao-Yan Lu Zu-Xuan Wang Yao-Ming Liang Shi-Yu Lin Yan-Hong Ji UJAT-Net: A U-Net Combined Joint-Attention and Transformer for Breast Tubule Segmentation in H&E Stained Images IEEE Access Hematoxylin-eosin (H&E) stained images tubule segmentation U-Net attention mechanism |
| title | UJAT-Net: A U-Net Combined Joint-Attention and Transformer for Breast Tubule Segmentation in H&E Stained Images |
| title_full | UJAT-Net: A U-Net Combined Joint-Attention and Transformer for Breast Tubule Segmentation in H&E Stained Images |
| title_fullStr | UJAT-Net: A U-Net Combined Joint-Attention and Transformer for Breast Tubule Segmentation in H&E Stained Images |
| title_full_unstemmed | UJAT-Net: A U-Net Combined Joint-Attention and Transformer for Breast Tubule Segmentation in H&E Stained Images |
| title_short | UJAT-Net: A U-Net Combined Joint-Attention and Transformer for Breast Tubule Segmentation in H&E Stained Images |
| title_sort | ujat net a u net combined joint attention and transformer for breast tubule segmentation in h x0026 e stained images |
| topic | Hematoxylin-eosin (H&E) stained images tubule segmentation U-Net attention mechanism |
| url | https://ieeexplore.ieee.org/document/10445190/ |
| work_keys_str_mv | AT liwangliu ujatnetaunetcombinedjointattentionandtransformerforbreasttubulesegmentationinhx0026estainedimages AT zhaohuang ujatnetaunetcombinedjointattentionandtransformerforbreasttubulesegmentationinhx0026estainedimages AT kaoyanlu ujatnetaunetcombinedjointattentionandtransformerforbreasttubulesegmentationinhx0026estainedimages AT zuxuanwang ujatnetaunetcombinedjointattentionandtransformerforbreasttubulesegmentationinhx0026estainedimages AT yaomingliang ujatnetaunetcombinedjointattentionandtransformerforbreasttubulesegmentationinhx0026estainedimages AT shiyulin ujatnetaunetcombinedjointattentionandtransformerforbreasttubulesegmentationinhx0026estainedimages AT yanhongji ujatnetaunetcombinedjointattentionandtransformerforbreasttubulesegmentationinhx0026estainedimages |