Traditional staining normalization approaches, e.g. Macenko, typically rely on the choice of a single representative reference image, which may not adequately account for the diverse staining patterns of datasets collected in practical scenarios. In this study, we introduce a novel approach that leverages multiple reference images to enhance robustness against stain variation. Our method is parameter-free and can be adopted in existing computational pathology pipelines with no significant changes. We evaluate the effectiveness of our method through experiments using a deep-learning pipeline for automatic nuclei segmentation on colorectal images. Our results show that by leveraging multiple reference images, better results can be achieved when generalizing to external data, where the staining can widely differ from the training set.

翻译：传统的染色归一化方法（如Macenko方法）通常依赖于选择单一代表性参考图像，这可能无法充分适应实际场景中采集数据集的多样化染色模式。本研究提出一种利用多张参考图像的新方法，以增强对染色变化的鲁棒性。该方法无需参数调整，可直接应用于现有计算病理学流程而无需重大改动。我们通过结直肠图像自动细胞核分割的深度学习流程实验评估了本方法的有效性。结果表明，在推广至染色特征与训练集存在显著差异的外部数据时，利用多参考图像能够获得更优的性能表现。