Multi-target stain normalization for histology slides

TL;DR

This paper presents a multi-target stain normalization method for histology slides that improves robustness and generalization across diverse staining patterns by using multiple reference images, without adding complexity to existing pipelines.

Contribution

The authors introduce a parameter-free multi-reference stain normalization approach that enhances stain variability handling in computational pathology workflows.

Findings

Improved nuclei segmentation accuracy on external colorectal datasets.

Better generalization to diverse staining patterns.

Compatible with existing deep-learning pipelines.

Abstract

Traditional staining normalization approaches, e.g. Macenko, typically rely on the choice of a single representative reference image, which may not adequately account for the diverse staining patterns of datasets collected in practical scenarios. In this study, we introduce a novel approach that leverages multiple reference images to enhance robustness against stain variation. Our method is parameter-free and can be adopted in existing computational pathology pipelines with no significant changes. We evaluate the effectiveness of our method through experiments using a deep-learning pipeline for automatic nuclei segmentation on colorectal images. Our results show that by leveraging multiple reference images, better results can be achieved when generalizing to external data, where the staining can widely differ from the training set.