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.

翻译：肾脏病理学作为肾脏疾病诊断的金标准，需要医生分别分析经H&E染色及马森、PASM、PAS等特殊染色的连续组织切片。这些特殊染色方法成本高、耗时长且难以标准化，尤其在基层医院难以推广。监督学习方法可将H&E图像虚拟转换为特殊染色图像，但像素级对齐难以实现训练。相反，将不同染色视为不同风格迁移域的无监督学习方法可使用非配对数据，但忽略了空间域间相关性，从而降低了诊断所需结构细节的可靠性。本文提出一种新型虚拟染色框架AGMDT，通过避免像素级对齐同时利用相邻组织切片间的相关性实现跨域图像转换。我们首先构建了一个高质量的多域肾脏组织学数据集，其中每个标本病例包含一系列经不同方式染色的连续切片。基于此，所提出的AGMDT框架通过肾小球检测和二分图匹配发现多域连续切片间的补丁级对齐对，并利用此类相关性监督端到端模型进行多域染色转换。实验结果表明，所提出的AGMDT通过利用多域连续病理切片间的相关性，在精确像素级对齐与非配对域迁移之间取得了良好平衡，并在定量指标和形态细节方面均优于现有最优方法。