Generative Adversarial Networks for Facilitating Stain-Independent Supervised and Unsupervised Segmentation: A Study...

Area of Science:

• Digital Pathology
• Medical Image Analysis
• Computer Vision

Background:

• High manual annotation effort and image variability are major challenges in digital pathology segmentation.
• Existing methods struggle with domain variability without extensive sample annotation.

Purpose of the Study:

• To develop stain-independent segmentation methods for digital pathology.
• To reduce reliance on manual annotations through advanced image-to-image translation techniques.

Main Methods:

• Utilized adversarial models for unpaired image-to-image translation.
• Developed stain-independent supervised segmentation via intermediate representations.
• Created a fully unsupervised segmentation approach using image-to-image translation for domain adaptation.

Main Results:

• Demonstrated effective stain-translation for domain adaptation in kidney histology.
• Achieved stain independence and improved segmentation accuracy.
• Combined domain adaptation with unsupervised segmentation for significant performance boosts.

Conclusions:

• Adversarial image-to-image translation effectively addresses stain variability in digital pathology.
• The proposed methods enhance unsupervised segmentation performance and reduce annotation burden.
• This work offers a promising direction for automated and robust digital pathology analysis.