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Prostate Cancer Characterization Based on Rapid SHG Imaging of Collagen Fiber Combined With Denoising Algorithm.

Jia He1, Xinpeng Huang1, Yating Fang1

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Rapid deep learning denoising enhances low-signal Second Harmonic Generation (SHG) imaging for prostate cancer (PCa) diagnosis. This improves collagen fiber analysis, enabling faster, more accurate optical histopathology.

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Gleason patterncollagendeep learning denoisingfast imagingprostate cancersecond harmonic generation (SHG)

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Area of Science:

  • Biomedical optics
  • Medical imaging
  • Computational pathology

Background:

  • Second Harmonic Generation (SHG) imaging visualizes collagen fibers, crucial for prostate cancer (PCa) diagnosis.
  • High signal-to-noise ratio (SNR) SHG imaging demands lengthy acquisition, hindering clinical application.
  • Gleason grading is standard for PCa assessment, relying on histological features.

Purpose of the Study:

  • To develop a rapid SHG imaging method for PCa assessment.
  • To enhance image quality and preserve diagnostic features from low-SNR SHG data.
  • To enable robust collagen orientation analysis with reduced acquisition times.

Main Methods:

  • Implemented rapid SHG imaging combined with a deep-learning denoising network (Selective Residual M-Net, SRMNet).
  • Reconstructed high-fidelity SHG images from low-SNR inputs.
  • Quantitatively assessed collagen alignment and stromal organization.

Main Results:

  • The denoising network significantly improved SHG image quality.
  • Collagen alignment features crucial for quantitative assessment were preserved.
  • Accurate collagen orientation metrics were extracted under low-SNR conditions, reducing acquisition time.

Conclusions:

  • Deep learning-assisted SHG imaging offers an effective strategy for reliable collagen orientation analysis in PCa.
  • This approach supports the development of rapid and objective optical histopathology for cancer diagnosis.
  • Enhanced SHG imaging improves diagnostic feasibility by reducing acquisition time while maintaining image fidelity.