  Renal pathology, as the gold standard of kidney disease diagnosis, requires
doctors to analyze a series of tissue slices stained by H&E staining and
special staining like Masson, PASM, and PAS, respectively. These special
staining methods are costly, time-consuming, and hard to standardize for wide
use especially in primary hospitals. Advances of supervised learning methods
have enabled the virtually conversion of H&E images into special staining
images, but achieving pixel-to-pixel alignment for training remains
challenging. In contrast, unsupervised learning methods regarding different
stains as different style transfer domains can utilize unpaired data, but they
ignore the spatial inter-domain correlations and thus decrease the
trustworthiness of structural details for diagnosis. In this paper, we propose
a novel virtual staining framework AGMDT to translate images into other domains
by avoiding pixel-level alignment and meanwhile utilizing the correlations
among adjacent tissue slices. We first build a high-quality multi-domain renal
histological dataset where each specimen case comprises a series of slices
stained in various ways. Based on it, the proposed framework AGMDT discovers
patch-level aligned pairs across the serial slices of multi-domains through
glomerulus detection and bipartite graph matching, and utilizes such
correlations to supervise the end-to-end model for multi-domain staining
transformation. Experimental results show that the proposed AGMDT achieves a
good balance between the precise pixel-level alignment and unpaired domain
transfer by exploiting correlations across multi-domain serial pathological
slices, and outperforms the state-of-the-art methods in both quantitative
measure and morphological details.
