Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation
Abstract Purpose Fluorescence in situ hybridization (FISH) plays a critical role in cancer screening but faces challenges in signal clarity and manual intervention. This study aims to enhance FISH signal clarity, improve screening efficiency, and reduce false negatives through an automated image acq...
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| Format: | Article |
| Language: | English |
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Springer
2025-03-01
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| Series: | Journal of Cancer Research and Clinical Oncology |
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| Online Access: | https://doi.org/10.1007/s00432-025-06150-9 |
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| _version_ | 1849699287790780416 |
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| author | Lemin Shi Ping Gong Mingye Li Dianxin Song Hao Zhang Zhe Wang Xin Feng |
| author_facet | Lemin Shi Ping Gong Mingye Li Dianxin Song Hao Zhang Zhe Wang Xin Feng |
| author_sort | Lemin Shi |
| collection | DOAJ |
| description | Abstract Purpose Fluorescence in situ hybridization (FISH) plays a critical role in cancer screening but faces challenges in signal clarity and manual intervention. This study aims to enhance FISH signal clarity, improve screening efficiency, and reduce false negatives through an automated image acquisition and signal enhancement framework. Methods An automated workflow was developed, integrating a dynamic signal enhancement method that optimizes global and local features. An improved Cycle-GAN network was introduced, incorporating residual connections and layer-wise supervision to accurately model and compensate for complex signal characteristics. Key metrics such as signal brightness, edge gradients, contrast improvement index (CII), and structural similarity index (SSIM) were used to evaluate performance. Results The proposed method increased weak signal brightness by 49.02%, edge gradients by 48.61%, and CII by 32.52%. The SSIM reached 0.996, indicating high fidelity to original signals. Conclusion Visual analysis demonstrated clearer, more continuous, and uniform fluorescence signals, effectively mitigating fragmentation and uneven distribution. These improvements reduced false negatives and enhanced genomic abnormality detection accuracy. The proposed method significantly improves FISH signal clarity and stability, providing reliable support for cancer screening, genomic abnormality detection, molecular typing, prognosis evaluation, and targeted treatment planning. |
| format | Article |
| id | doaj-art-dff6842f8bb5429d853d84ce4c27829c |
| institution | DOAJ |
| issn | 1432-1335 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of Cancer Research and Clinical Oncology |
| spelling | doaj-art-dff6842f8bb5429d853d84ce4c27829c2025-08-20T03:18:38ZengSpringerJournal of Cancer Research and Clinical Oncology1432-13352025-03-01151311210.1007/s00432-025-06150-9Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensationLemin Shi0Ping Gong1Mingye Li2Dianxin Song3Hao Zhang4Zhe Wang5Xin Feng6School of Computer Science and Technology, Changchun University of Science and TechnologySchool of Life Science and Technology, Changchun University of Science and TechnologyDepartment of Information Systems and Business Analytics, RMIT UniversitySchool of Life Science and Technology, Changchun University of Science and TechnologySchool of Life Science and Technology, Changchun University of Science and TechnologySchool of Life Science and Technology, Changchun University of Science and TechnologySchool of Computer Science and Technology, Changchun University of Science and TechnologyAbstract Purpose Fluorescence in situ hybridization (FISH) plays a critical role in cancer screening but faces challenges in signal clarity and manual intervention. This study aims to enhance FISH signal clarity, improve screening efficiency, and reduce false negatives through an automated image acquisition and signal enhancement framework. Methods An automated workflow was developed, integrating a dynamic signal enhancement method that optimizes global and local features. An improved Cycle-GAN network was introduced, incorporating residual connections and layer-wise supervision to accurately model and compensate for complex signal characteristics. Key metrics such as signal brightness, edge gradients, contrast improvement index (CII), and structural similarity index (SSIM) were used to evaluate performance. Results The proposed method increased weak signal brightness by 49.02%, edge gradients by 48.61%, and CII by 32.52%. The SSIM reached 0.996, indicating high fidelity to original signals. Conclusion Visual analysis demonstrated clearer, more continuous, and uniform fluorescence signals, effectively mitigating fragmentation and uneven distribution. These improvements reduced false negatives and enhanced genomic abnormality detection accuracy. The proposed method significantly improves FISH signal clarity and stability, providing reliable support for cancer screening, genomic abnormality detection, molecular typing, prognosis evaluation, and targeted treatment planning.https://doi.org/10.1007/s00432-025-06150-9Fluorescence in situ hybridization (FISH); feature enhancement; cyclic generative adversarial network (Cycle-GAN); cancer screening |
| spellingShingle | Lemin Shi Ping Gong Mingye Li Dianxin Song Hao Zhang Zhe Wang Xin Feng Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation Journal of Cancer Research and Clinical Oncology Fluorescence in situ hybridization (FISH); feature enhancement; cyclic generative adversarial network (Cycle-GAN); cancer screening |
| title | Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation |
| title_full | Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation |
| title_fullStr | Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation |
| title_full_unstemmed | Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation |
| title_short | Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation |
| title_sort | chronic lymphocytic leukemia cll screening and abnormality detection based on multi layer fluorescence imaging signal enhancement and compensation |
| topic | Fluorescence in situ hybridization (FISH); feature enhancement; cyclic generative adversarial network (Cycle-GAN); cancer screening |
| url | https://doi.org/10.1007/s00432-025-06150-9 |
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