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Updated: Jun 19, 2026

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Dynamic Frequency-Decoupled Refinement Network for Polyp Segmentation.

Yao Tong1,2, Jingxian Chai1, Ziqi Chen3

  • 1School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China.

Bioengineering (Basel, Switzerland)
|March 28, 2025
PubMed
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This summary is machine-generated.

We developed a new AI method for polyp segmentation in colorectal cancer screening. Our Dynamic Frequency-Decoupled Refinement Network (DFDRNet) improves accuracy by processing image data in both frequency and spatial domains.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Accurate polyp segmentation is vital for early colorectal cancer detection.
  • Challenges include polyp variability (size, shape, texture) and low contrast.
  • Current spatial-domain methods struggle with feature separation, limiting segmentation performance.

Purpose of the Study:

  • To introduce a novel segmentation framework, the Dynamic Frequency-Decoupled Refinement Network (DFDRNet).
  • To enhance polyp segmentation accuracy by integrating frequency-domain and spatial-domain processing.
  • To improve the robustness and efficiency of polyp detection in medical imaging.

Main Methods:

  • Developed the Frequency Adaptive Decoupling (FAD) module for dynamic separation of high- and low-frequency image components.
Keywords:
attention mechanismcolonoscopy imageneural networkpolyp segmentation

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  • Introduced the Frequency Adaptive Refinement (FAR) module to refine frequency components before spatial feature fusion.
  • Integrated these modules within a U-shaped encoder-decoder architecture for polyp segmentation.

    • Main Results:

    • DFDRNet achieved state-of-the-art performance on three benchmark datasets for polyp segmentation.
    • The method demonstrated superior robustness and computational efficiency compared to existing approaches.
    • Evaluations confirmed the effectiveness of frequency-domain integration for enhancing segmentation accuracy.

    Conclusions:

    • DFDRNet offers a significant advancement in AI-driven polyp segmentation for colorectal cancer screening.
    • The proposed frequency-domain and spatial-domain integration effectively addresses segmentation challenges.
    • DFDRNet provides a promising tool for improving early cancer detection with enhanced accuracy and efficiency.