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Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation.

Qun Li1, Linlin Liu2

  • 1School of Electronic Information Engineering, Ningbo Polytechnic, Ningbo 315800, China.

Computational Intelligence and Neuroscience
|June 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an artificial intelligence semisupervised self-training algorithm for pathological tissue image segmentation. The novel Seg cutting method demonstrates superior performance in segmenting retinal blood vessels in both healthy and diabetic patients.

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Area of Science:

  • Medical Image Processing
  • Artificial Intelligence in Medicine
  • Pathological Tissue Analysis

Background:

  • Medical images exhibit significant variations due to tissue, organ, and imaging method differences.
  • Traditional cluster-based image segmentation suffers from high randomness in initialization class centers.
  • Artificial intelligence (AI) algorithms are increasingly applied to medical computing optimization.

Purpose of the Study:

  • To integrate an AI-based semisupervised self-training algorithm into pathological tissue image segmentation.
  • To address the limitations of traditional image segmentation methods.
  • To improve the accuracy and visual quality of pathological image segmentation.

Main Methods:

  • Developed a novel "Seg cutting" method integrating AI semisupervised self-training.
  • Applied the algorithm to pathological tissue image segmentation, specifically retinal blood vessel segmentation.
  • Utilized databases of healthy individuals and diabetic patients for experimental validation.

Main Results:

  • The Seg cutting method achieved high sensitivity (0.9416), specificity (0.9568), and accuracy (0.9618) in healthy retinal blood vessel segmentation.
  • In diabetic patients, the method showed sensitivity (0.8106), specificity (0.9712), and accuracy (0.9421), with a low false-positive rate (0.0511).
  • Performance metrics surpassed those of existing methods like FNN, CNN, and AWN.

Conclusions:

  • The AI-based semisupervised self-training algorithm effectively performs pathological tissue image segmentation.
  • The Seg cutting method offers improved accuracy and efficiency compared to conventional techniques.
  • This approach enhances the application of AI in medical image analysis and pathological diagnostics.