Context-based Normalization of Histological Stains using Deep Convolutional Features

TL;DR

This paper presents a novel deep learning approach for histological stain normalization that considers tissue texture context, improving consistency and performance of digital pathology algorithms with limited labeled data.

Contribution

It introduces Feature Aware Normalization, combining batch normalization with LSTM gating and pretrained deep features for end-to-end stain normalization.

Findings

Achieves superior color and texture consistency.

Reduces histogram deviations compared to existing methods.

Ensures stable and reproducible stain normalization results.

Abstract

While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available. This work provides a fully automated, end-to-end learning-based setup for normalizing histological stains, which considers the texture context of the tissue. We introduce Feature Aware Normalization, which extends the framework of batch normalization in combination with gating elements from Long Short-Term Memory units for normalization among different spatial regions of interest. By incorporating a pretrained deep neural network as a feature extractor steering a pixelwise processing pipeline, we achieve excellent normalization results and ensure a consistent representation of color and texture. The evaluation comprises a comparison of color histogram deviations, structural similarity and measures the color volume obtained by the different methods.