StainStyleSampler: clustering-based sampling of whole slide image appearances

Area of Science:

• Digital Pathology
• Computational Pathology
• Machine Learning in Histology

Background:

• Whole slide biopsy images exhibit significant variations due to laboratory procedures and digital slide scanners.
• These stain variations are a major obstacle for developing generalizable machine learning algorithms in digital pathology.
• Existing stain normalization and augmentation techniques offer limited modeling of stain style distributions.

Purpose of the Study:

• To present StainStyleSampler, a toolkit for exploring, modeling, and sampling stain style variations in histological images.
• To enable explicit evaluation of machine learning robustness across different stain styles.
• To provide tools for pathologists and computer scientists to better understand and utilize stain variation.

Main Methods:

• Extraction of color features and deconvolved stain components from whole slide images.
• Visualization of extracted features, including dimensionality reduction techniques.
• Modeling of stain style distributions using binning, clustering, and density mapping.
• Automated sampling of representative reference images capturing core stain variations.

Main Results:

• The StainStyleSampler facilitates the exploration and modeling of stain style variations.
• The toolkit enables automated sampling of images representing key aspects of stain variation.
• It provides a streamlined approach to understanding and quantifying stain differences.

Conclusions:

• StainStyleSampler offers a versatile solution for exploring and sampling stain variations in whole slide images.
• The software can significantly aid in developing more robust machine learning models for digital pathology.
• It empowers researchers to better assess and manage the impact of stain variability on AI algorithms.