Conditional generative adversarial networks for H&E to if domain transfer: Experiments with breast and prostate cancer

In this work, we explore image-to-image translation using Conditional Generative Adversarial Networks (cGAN) to convert digital tissue images from the brightfield to the immunofluorescence (IF) domain. A dataset of 149 tissue microarray (TMA) cores were stained using a multiplexed IF system for DAPI, Ribosomal S6, and NaKATPase. These TMA cores were subsequently stained with hematoxylin and eosin (H&E) and digitally scanned. Using registered pairs of H&E and IF, a cGAN was trained to translate from the H&E to the IF domain for DAPI, Ribosomal S6, and NaKATPase markers. This classifier was then evaluated by translating a set of holdout H&E samples, both from the original TMA dataset as well as an independent prostate cancer H&E dataset (for which we do not have IF probes). The cGAN was evaluated quantitatively for our multiplexed TMA samples and qualitatively for the independent H&E dataset. We found that for the DAPI channel, the cGAN is able to produce accurate samples but is unable to replicate the subtle pixel intensity differences that characterize boundaries between nuclei. For the NaKATPase and Ribosomal S6 channels, the cGAN over segmented extracellular matrix regions. On the holdout open-source H&E stained prostate tissue dataset, the cGAN produced qualitatively acceptable results.