MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation
In the medical field, hematoxylin and eosin (H&E)-stained histopathology images of cell nuclei analysis represent an important measure for cancer diagnosis. The most valuable aspect of the nuclei analysis is the segmentation of the different nuclei morphologies of different organs and subsequent...
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/8/4842 |
| _version_ | 1827746030356004864 |
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| author | Jiangqi Li Xiang Li |
| author_facet | Jiangqi Li Xiang Li |
| author_sort | Jiangqi Li |
| collection | DOAJ |
| description | In the medical field, hematoxylin and eosin (H&E)-stained histopathology images of cell nuclei analysis represent an important measure for cancer diagnosis. The most valuable aspect of the nuclei analysis is the segmentation of the different nuclei morphologies of different organs and subsequent diagnosis of the type and severity of the disease based on pathology. In recent years, deep learning techniques have been widely used in digital histopathology analysis. Automated nuclear segmentation technology enables the rapid and efficient segmentation of tens of thousands of complex and variable nuclei in histopathology images. However, a challenging problem during nuclei segmentation is the blocking of cell nuclei, overlapping, and background complexity of the tissue fraction. To address this challenge, we present MIU-net, an efficient deep learning network structure for the nuclei segmentation of histopathology images. Our proposed structure includes two blocks with modified inception module and attention module. The advantage of the modified inception module is to balance the computation and network performance of the deeper layers of the network, combined with the convolutional layer using different sizes of kernels to learn effective features in a fast and efficient manner to complete kernel segmentation. The attention module allows us to extract small and fine irregular boundary features from the images, which can better segment cancer cells that appear disorganized and fragmented. We test our methodology on public kumar datasets and achieve the highest AUC score of 0.92. The experimental results show that the proposed method achieves better performance than other state-of-the-art methods. |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:16:45Z |
| publishDate | 2023-04-01 |
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| spelling | doaj.art-3940f243233349518066979bc8a95ced2023-11-17T18:10:02ZengMDPI AGApplied Sciences2076-34172023-04-01138484210.3390/app13084842MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei SegmentationJiangqi Li0Xiang Li1College of Mathematical Sciences, Bohai University, Jinzhou 121000, ChinaDepartment of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaIn the medical field, hematoxylin and eosin (H&E)-stained histopathology images of cell nuclei analysis represent an important measure for cancer diagnosis. The most valuable aspect of the nuclei analysis is the segmentation of the different nuclei morphologies of different organs and subsequent diagnosis of the type and severity of the disease based on pathology. In recent years, deep learning techniques have been widely used in digital histopathology analysis. Automated nuclear segmentation technology enables the rapid and efficient segmentation of tens of thousands of complex and variable nuclei in histopathology images. However, a challenging problem during nuclei segmentation is the blocking of cell nuclei, overlapping, and background complexity of the tissue fraction. To address this challenge, we present MIU-net, an efficient deep learning network structure for the nuclei segmentation of histopathology images. Our proposed structure includes two blocks with modified inception module and attention module. The advantage of the modified inception module is to balance the computation and network performance of the deeper layers of the network, combined with the convolutional layer using different sizes of kernels to learn effective features in a fast and efficient manner to complete kernel segmentation. The attention module allows us to extract small and fine irregular boundary features from the images, which can better segment cancer cells that appear disorganized and fragmented. We test our methodology on public kumar datasets and achieve the highest AUC score of 0.92. The experimental results show that the proposed method achieves better performance than other state-of-the-art methods.https://www.mdpi.com/2076-3417/13/8/4842nuclei segmentationhistopathology imageattention mechanismefficient network |
| spellingShingle | Jiangqi Li Xiang Li MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation Applied Sciences nuclei segmentation histopathology image attention mechanism efficient network |
| title | MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation |
| title_full | MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation |
| title_fullStr | MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation |
| title_full_unstemmed | MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation |
| title_short | MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation |
| title_sort | miu net mix attention and inception u net for histopathology image nuclei segmentation |
| topic | nuclei segmentation histopathology image attention mechanism efficient network |
| url | https://www.mdpi.com/2076-3417/13/8/4842 |
| work_keys_str_mv | AT jiangqili miunetmixattentionandinceptionunetforhistopathologyimagenucleisegmentation AT xiangli miunetmixattentionandinceptionunetforhistopathologyimagenucleisegmentation |