Renal pathology, as the gold standard of kidney disease diagnosis, requires
doctors to analyze a serial 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
can virtually convert H\&E images into special staining images, but the
pixel-to-pixel alignment is hard to achieve for training. As contrast,
unsupervised learning methods regarding different stains as different style
transferring domains can use 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.

我们提出了一种新颖的虚拟染色框架 AGMDT，通过避免像素级对齐并同时利用多个相邻组织切片之间的相关性，将图像转化为其他领域。实验证明，该方法在精确的像素级对齐和无配对领域转化之间取得了良好的平衡，并在定量测量和形态细节方面优于现有方法。