AGMDT：基于邻接引导多域迁移的肾脏组织学图像虚拟染色
AGMDT: Virtual Staining of Renal Histology Images with Adjacency-Guided Multi-Domain Transfer
AI总结：
本文提出AGMDT虚拟染色框架，通过肾小球检测与二分图匹配利用相邻多染色切片间的块级相关性，无需像素级对齐即可实现多域染色迁移，并在定量与形态学上优于现有方法。
摘要：
肾脏病理学作为肾脏疾病诊断的金标准，要求医生分别分析经H&E染色以及Masson、PASM和PAS等特殊染色的一系列组织切片。这些特殊染色方法成本高、耗时长，并且难以标准化以广泛使用，尤其是在基层医院。监督学习方法的进展使得将H&E图像虚拟转换为特殊染色图像成为可能，但实现用于训练的像素到像素对齐仍然具有挑战性。相比之下，将不同染色视为不同风格迁移域的无监督学习方法可以利用未配对数据，但它们忽略了空间域间相关性，从而降低了对诊断而言结构细节的可信度。在本文中，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相邻组织切片之间的相关性，将图像转换到其他域。我们首先构建了一个高质量的多域肾脏组织学数据集，其中每个样本病例包含一系列以不同方式染色的切片。在此基础上，所提出的AGMDT框架通过肾小球检测和二分图匹配，在多域连续切片中发现块级对齐配对，并利用这些相关性来监督用于多域染色变换的端到端模型。实验结果表明，所提出的AGMDT通过利用多域连续病理切片之间的相关性，在精确的像素级对齐和未配对域迁移之间取得了良好平衡，并在定量指标和形态学细节方面均优于最先进的方法。
英文摘要：
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.
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