HEMIT: H&E to Multiplex-immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator

TL;DR

HEMIT introduces a novel dataset and a dual-branch neural network architecture for translating H&E stained images into multiplex-immunohistochemistry images, enabling better tumor micro-environment analysis.

Contribution

The paper presents the first publicly available cellular-level aligned dataset for H&E to mIHC translation and proposes a dual-branch generator that outperforms existing models.

Findings

The dual-branch generator achieves higher SSIM, R, and PSNR scores.

HEMIT dataset enables advanced computational methods for histology analysis.

The approach improves stain translation quality for cancer tissue analysis.

Abstract

Computational analysis of multiplexed immunofluorescence histology data is emerging as an important method for understanding the tumour micro-environment in cancer. This work presents HEMIT, a dataset designed for translating Hematoxylin and Eosin (H&E) sections to multiplex-immunohistochemistry (mIHC) images, featuring DAPI, CD3, and panCK markers. Distinctively, HEMIT's mIHC images are multi-component and cellular-level aligned with H&E, enriching supervised stain translation tasks. To our knowledge, HEMIT is the first publicly available cellular-level aligned dataset that enables H&E to multi-target mIHC image translation. This dataset provides the computer vision community with a valuable resource to develop novel computational methods which have the potential to gain new insights from H&E slide archives. We also propose a new dual-branch generator architecture, using residual Convolutional Neural Networks (CNNs) and Swin Transformers which achieves better translation outcomes than other popular algorithms. When evaluated on HEMIT, it outperforms pix2pixHD, pix2pix, U-Net, and ResNet, achieving the highest overall score on key metrics including the Structural Similarity Index Measure (SSIM), Pearson correlation score (R), and Peak signal-to-noise Ratio (PSNR). Additionally, downstream analysis has been used to further validate the quality of the generated mIHC images. These results set a new benchmark in the field of stain translation tasks.