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RGGC-UNet: Accurate Deep Learning Framework for Signet Ring Cell Semantic Segmentation in Pathological Images.

Tengfei Zhao1, Chong Fu1,2,3, Wei Song1

  • 1School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.

Bioengineering (Basel, Switzerland)
|January 22, 2024
PubMed
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This summary is machine-generated.

This study introduces RGGC-UNet, an efficient deep learning model for segmenting Signet Ring Cells (SRCs) in pathological images. The model achieves high accuracy while reducing computational load, aiding in SRC carcinoma diagnosis.

Area of Science:

  • Medical image analysis
  • Computational pathology
  • Artificial intelligence in diagnostics

Background:

  • Semantic segmentation of Signet Ring Cells (SRCs) is crucial for diagnosing SRC carcinoma.
  • Deep learning shows promise in computer-aided diagnosis but often involves computationally intensive models.
  • Limited ground truth data for SRCs hinders segmentation technique development.

Purpose of the Study:

  • To develop an efficient and accurate deep learning framework for SRC semantic segmentation.
  • To address the computational overhead and data limitations in existing methods.
  • To improve the diagnostic accuracy of SRC carcinoma through enhanced image analysis.

Main Methods:

  • Introduction of RGGC-UNet, a UNet-based framework with a novel encoder.
Keywords:
ghost coordinate attentionresidual ghost blocksemantic segmentationsignet ring cell

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  • Utilized residual ghost blocks with ghost coordinate attention for computational efficiency.
  • Enriched the DigestPath 2019 dataset with fully annotated SRC mask labels.

    • Main Results:

    • The proposed RGGC-UNet model demonstrated superior segmentation accuracy compared to leading-edge models.
    • The model achieved significant reductions in computational overhead.
    • Experimental results validate the model's effectiveness and efficiency for pathological diagnosis.

    Conclusions:

    • RGGC-UNet offers an efficient and accurate solution for Signet Ring Cell semantic segmentation.
    • The framework effectively minimizes computational costs while maximizing segmentation performance.
    • This advancement holds potential for improving the accuracy and efficiency of SRC carcinoma diagnosis.