  With the advent of digital scanners and deep learning, diagnostic operations
may move from a microscope to a desktop. Hematoxylin and Eosin (H&E) staining
is one of the most frequently used stains for disease analysis, diagnosis, and
grading, but pathologists do need different immunohistochemical (IHC) stains to
analyze specific structures or cells. Obtaining all of these stains (H&E and
different IHCs) on a single specimen is a tedious and time-consuming task.
Consequently, virtual staining has emerged as an essential research direction.
Here, we propose a novel generative model, Structural Cycle-GAN (SC-GAN), for
synthesizing IHC stains from H&E images, and vice versa. Our method expressly
incorporates structural information in the form of edges (in addition to color
data) and employs attention modules exclusively in the decoder of the proposed
generator model. This integration enhances feature localization and preserves
contextual information during the generation process. In addition, a structural
loss is incorporated to ensure accurate structure alignment between the
generated and input markers. To demonstrate the efficacy of the proposed model,
experiments are conducted with two IHC markers emphasizing distinct structures
of glands in the colon: the nucleus of epithelial cells (CDX2) and the
cytoplasm (CK818). Quantitative metrics such as FID and SSIM are frequently
used for the analysis of generative models, but they do not correlate
explicitly with higher-quality virtual staining results. Therefore, we propose
two new quantitative metrics that correlate directly with the virtual staining
specificity of IHC markers.
