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Purpose <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月11日

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  • Lemin Shi,
  • Ping Gong,
  • Mingye Li,
  • Dianxin Song,
  • Hao Zhang,
  • Zhe Wang,
  • Xin Feng

摘要

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.