DeReStainer: H&E to IHC Pathological Image Translation via Decoupled Staining Channels

TL;DR

This paper introduces DeReStainer, a novel framework that converts H&E stained images to IHC images for breast cancer diagnosis, utilizing a decoupled staining channels approach to improve accuracy and semantic information preservation.

Contribution

The paper proposes a decoupled staining channels method for H&E to IHC image translation, with specialized loss functions and semantic metrics, advancing the accuracy of pathological image conversion.

Findings

Outperforms previous methods on benchmark metrics.

Generates IHC images with better intrinsic properties.

Preserves semantic information related to HER2 levels.

Abstract

Breast cancer is a highly fatal disease among cancers in women, and early detection is crucial for treatment. HER2 status, a valuable diagnostic marker based on Immunohistochemistry (IHC) staining, is instrumental in determining breast cancer status. The high cost of IHC staining and the ubiquity of Hematoxylin and Eosin (H&E) staining make the conversion from H&E to IHC staining essential. In this article, we propose a destain-restain framework for converting H&E staining to IHC staining, leveraging the characteristic that H&E staining and IHC staining of the same tissue sections share the Hematoxylin channel. We further design loss functions specifically for Hematoxylin and Diaminobenzidin (DAB) channels to generate IHC images exploiting insights from separated staining channels. Beyond the benchmark metrics on BCI contest, we have developed semantic information metrics for the HER2 level. The experimental results demonstrated that our method outperforms previous open-sourced methods in terms of image intrinsic property and semantic information.