Intelligent cell images segmentation system: based on SDN and moving transformer
Abstract Diagnosing diseases heavily relies on cell pathology images, but the extensive data in each manual identification of relevant cells labor-intensive, especially in regions with a scarcity of qualified healthcare professionals. This study aims to develop an intelligent system to enhance the d...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-76577-6 |
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| author | Jia Wu Yao Pan Qing Ye Jing Zhou Fangfang Gou |
| author_facet | Jia Wu Yao Pan Qing Ye Jing Zhou Fangfang Gou |
| author_sort | Jia Wu |
| collection | DOAJ |
| description | Abstract Diagnosing diseases heavily relies on cell pathology images, but the extensive data in each manual identification of relevant cells labor-intensive, especially in regions with a scarcity of qualified healthcare professionals. This study aims to develop an intelligent system to enhance the diagnostic accuracy of cytopathology images by addressing image noise and segmentation issues, thereby improving the efficiency of medical professionals in disease diagnosis. We introduced an innovative system combining a self-supervised algorithm, SDN, for image denoising with data enhancement and image segmentation using the UPerMVit model. The UPerMVit model’s novel attention mechanisms and modular architecture provide higher accuracy and lower computational complexity than traditional methods. The proposed system effectively reduces image noise and accurately segments annotated images, highlighting cellular structures relevant to medical staff. This enhances diagnostic accuracy and aids in the accurate identification of pathological cells. Our intelligent system offers a reliable tool for medical professionals, improving diagnostic efficiency and accuracy in cytopathologic image analysis. It provides significant technical support in regions lacking adequate medical expertise. |
| format | Article |
| id | doaj-art-871f06570e214565a8f7da36263bee92 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-871f06570e214565a8f7da36263bee922025-08-20T02:11:21ZengNature PortfolioScientific Reports2045-23222024-10-0114112010.1038/s41598-024-76577-6Intelligent cell images segmentation system: based on SDN and moving transformerJia Wu0Yao Pan1Qing Ye2Jing Zhou3Fangfang Gou4School of Computer Science and Technology, Jiangxi University of Chinese MedicineSchool of Computer Science and Technology, Jiangxi University of Chinese MedicineSchool of Computer Science and Technology, Jiangxi University of Chinese MedicineHunan University of Medicine General HospitalState Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou UniversityAbstract Diagnosing diseases heavily relies on cell pathology images, but the extensive data in each manual identification of relevant cells labor-intensive, especially in regions with a scarcity of qualified healthcare professionals. This study aims to develop an intelligent system to enhance the diagnostic accuracy of cytopathology images by addressing image noise and segmentation issues, thereby improving the efficiency of medical professionals in disease diagnosis. We introduced an innovative system combining a self-supervised algorithm, SDN, for image denoising with data enhancement and image segmentation using the UPerMVit model. The UPerMVit model’s novel attention mechanisms and modular architecture provide higher accuracy and lower computational complexity than traditional methods. The proposed system effectively reduces image noise and accurately segments annotated images, highlighting cellular structures relevant to medical staff. This enhances diagnostic accuracy and aids in the accurate identification of pathological cells. Our intelligent system offers a reliable tool for medical professionals, improving diagnostic efficiency and accuracy in cytopathologic image analysis. It provides significant technical support in regions lacking adequate medical expertise.https://doi.org/10.1038/s41598-024-76577-6Image segmentationArtificial intelligenceCell pathology imagesSelf-supervised denoisingMedical assistance system |
| spellingShingle | Jia Wu Yao Pan Qing Ye Jing Zhou Fangfang Gou Intelligent cell images segmentation system: based on SDN and moving transformer Scientific Reports Image segmentation Artificial intelligence Cell pathology images Self-supervised denoising Medical assistance system |
| title | Intelligent cell images segmentation system: based on SDN and moving transformer |
| title_full | Intelligent cell images segmentation system: based on SDN and moving transformer |
| title_fullStr | Intelligent cell images segmentation system: based on SDN and moving transformer |
| title_full_unstemmed | Intelligent cell images segmentation system: based on SDN and moving transformer |
| title_short | Intelligent cell images segmentation system: based on SDN and moving transformer |
| title_sort | intelligent cell images segmentation system based on sdn and moving transformer |
| topic | Image segmentation Artificial intelligence Cell pathology images Self-supervised denoising Medical assistance system |
| url | https://doi.org/10.1038/s41598-024-76577-6 |
| work_keys_str_mv | AT jiawu intelligentcellimagessegmentationsystembasedonsdnandmovingtransformer AT yaopan intelligentcellimagessegmentationsystembasedonsdnandmovingtransformer AT qingye intelligentcellimagessegmentationsystembasedonsdnandmovingtransformer AT jingzhou intelligentcellimagessegmentationsystembasedonsdnandmovingtransformer AT fangfanggou intelligentcellimagessegmentationsystembasedonsdnandmovingtransformer |