组织病理学中的染色标准化：使用多中心数据集进行方法基准测试
Staining normalization in histopathology: Method benchmarking using multicenter dataset
AI总结：
本研究构建多中心组织图像数据集，系统比较8种染色标准化方法在减少H&E染色变异上的表现，为提升AI模型泛化能力提供基准。
摘要：
几十年来，苏木精和伊红（H&E）染色一直是组织分析的黄金标准，然而，在不同实验室染色的组织标本在外观上通常存在显著差异。这种变异对病理学家和基于AI的下游分析均构成了挑战。通过计算方法最小化染色变异是一个活跃的研究领域。为了进一步研究这一问题，我们收集了一个独特的多中心组织图像数据集，其中来自结肠、肾脏和皮肤组织块的组织样本被分发至66个不同的实验室进行常规H&E染色。为了分离染色变异，影响组织外观的其他因素保持不变。此外，我们利用该组织图像数据集比较了八种不同染色标准化方法的性能，包括四种传统方法，即直方图匹配、Macenko、Vahadane和Reinhard标准化，以及两种基于深度学习的方法，即CycleGAN和Pixp2pix，两者各有两个变体。我们使用定量和定性评估来衡量这些方法的性能。该数据集的实验室间染色变异也可以指导通过多样化的训练数据来提升模型泛化能力的策略。
英文摘要：
Hematoxylin and Eosin (H&E) has been the gold standard in tissue analysis for decades, however, tissue specimens stained in different laboratories vary, often significantly, in appearance. This variation poses a challenge for both pathologists' and AI-based downstream analysis. Minimizing stain variation computationally is an active area of research. To further investigate this problem, we collected a unique multi-center tissue image dataset, wherein tissue samples from colon, kidney, and skin tissue blocks were distributed to 66 different labs for routine H&E staining. To isolate staining variation, other factors affecting the tissue appearance were kept constant. Further, we used this tissue image dataset to compare the performance of eight different stain normalization methods, including four traditional methods, namely, histogram matching, Macenko, Vahadane, and Reinhard normalization, and two deep learning-based methods namely CycleGAN and Pixp2pix, both with two variants each. We used both quantitative and qualitative evaluation to assess the performance of these methods. The dataset's inter-laboratory staining variation could also guide strategies to improve model generalizability through varied training data
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