An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation
Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely detect and localize nucleic acid sequences. This technique is proving to be an invaluable tool in medical diagnostics and has made significant contributions to biology and the life sciences. However, the num...
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/3/928 |
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| author | Zini Jian Tianxiang Song Zhihui Zhang Zhao Ai Heng Zhao Man Tang Kan Liu |
| author_facet | Zini Jian Tianxiang Song Zhihui Zhang Zhao Ai Heng Zhao Man Tang Kan Liu |
| author_sort | Zini Jian |
| collection | DOAJ |
| description | Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely detect and localize nucleic acid sequences. This technique is proving to be an invaluable tool in medical diagnostics and has made significant contributions to biology and the life sciences. However, the number of cells is large and the nucleic acid sequences are disorganized in the FISH images taken using the microscope. Processing and analyzing images is a time-consuming and laborious task for researchers, as it can easily tire the human eyes and lead to errors in judgment. In recent years, deep learning has made significant progress in the field of medical imaging, especially the successful application of introducing the attention mechanism. The attention mechanism, as a key component of deep learning, improves the understanding and interpretation of medical images by giving different weights to different regions of the image, enabling the model to focus more on important features. To address the challenges in FISH image analysis, we combined medical imaging with deep learning to develop the SEAM-Unet++ automated cell contour segmentation algorithm with integrated attention mechanism. The significant advantage of this algorithm is that it improves the accuracy of cell contours in FISH images. Experiments have demonstrated that by introducing the attention mechanism, our method is able to segment cells that are adherent to each other more efficiently. |
| first_indexed | 2024-03-08T03:48:57Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:48:57Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-91d031b70a964ce3ae24cf65754867402024-02-09T15:22:17ZengMDPI AGSensors1424-82202024-01-0124392810.3390/s24030928An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image SegmentationZini Jian0Tianxiang Song1Zhihui Zhang2Zhao Ai3Heng Zhao4Man Tang5Kan Liu6School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, ChinaSchool of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, ChinaSchool of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, ChinaSchool of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, ChinaSchool of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, ChinaSchool of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, ChinaSchool of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, ChinaFluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely detect and localize nucleic acid sequences. This technique is proving to be an invaluable tool in medical diagnostics and has made significant contributions to biology and the life sciences. However, the number of cells is large and the nucleic acid sequences are disorganized in the FISH images taken using the microscope. Processing and analyzing images is a time-consuming and laborious task for researchers, as it can easily tire the human eyes and lead to errors in judgment. In recent years, deep learning has made significant progress in the field of medical imaging, especially the successful application of introducing the attention mechanism. The attention mechanism, as a key component of deep learning, improves the understanding and interpretation of medical images by giving different weights to different regions of the image, enabling the model to focus more on important features. To address the challenges in FISH image analysis, we combined medical imaging with deep learning to develop the SEAM-Unet++ automated cell contour segmentation algorithm with integrated attention mechanism. The significant advantage of this algorithm is that it improves the accuracy of cell contours in FISH images. Experiments have demonstrated that by introducing the attention mechanism, our method is able to segment cells that are adherent to each other more efficiently.https://www.mdpi.com/1424-8220/24/3/928FISH imagescell contourssegmentationUnet++attention |
| spellingShingle | Zini Jian Tianxiang Song Zhihui Zhang Zhao Ai Heng Zhao Man Tang Kan Liu An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation Sensors FISH images cell contours segmentation Unet++ attention |
| title | An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation |
| title_full | An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation |
| title_fullStr | An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation |
| title_full_unstemmed | An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation |
| title_short | An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation |
| title_sort | improved nested u net network for fluorescence in situ hybridization cell image segmentation |
| topic | FISH images cell contours segmentation Unet++ attention |
| url | https://www.mdpi.com/1424-8220/24/3/928 |
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