HEMIT: H&E to Multiplex-immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator
This provides a valuable resource for the computer vision community to develop computational methods for analyzing cancer tumor micro-environments from H&E slide archives, though it is incremental as it builds on existing stain translation tasks.
The authors tackled the problem of translating H&E histology images to multiplex-immunohistochemistry (mIHC) images by introducing HEMIT, the first publicly available cellular-level aligned dataset for this task, and a dual-branch generator that outperforms existing methods, achieving the highest scores on SSIM, Pearson correlation, and PSNR metrics.
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.