Stain normalization algorithms aim to transform the color and intensity characteristics of a source multi-gigapixel histology image to match those of a target image, mitigating inconsistencies in the appearance of stains used to highlight cellular components in the images. We propose a new approach, StainFuser, which treats this problem as a style transfer task using a novel Conditional Latent Diffusion architecture, eliminating the need for handcrafted color components. With this method, we curate SPI-2M the largest stain normalization dataset to date of over 2 million histology images with neural style transfer for high-quality transformations. Trained on this data, StainFuser outperforms current state-of-the-art GAN and handcrafted methods in terms of the quality of normalized images. Additionally, compared to existing approaches, it improves the performance of nuclei instance segmentation and classification models when used as a test time augmentation method on the challenging CoNIC dataset. Finally, we apply StainFuser on multi-gigapixel Whole Slide Images (WSIs) and demonstrate improved performance in terms of computational efficiency, image quality and consistency across tiles over current methods.

翻译：染色标准化算法旨在将源多千兆像素组织学图像的颜色和强度特征转换为与目标图像相匹配，以减轻用于突出图像中细胞成分的染色外观不一致性。我们提出了一种新方法StainFuser，该方法将这一任务视为一种风格迁移任务，采用新颖的条件潜在扩散架构，无需手动设计的颜色成分。通过此方法，我们构建了SPI-2M——迄今为止最大的染色标准化数据集，包含超过200万张组织学图像，并通过神经风格迁移实现高质量变换。基于该数据训练的StainFuser在标准化图像质量方面优于当前最先进的生成对抗网络（GAN）和手工方法。此外，与现有方法相比，当在具有挑战性的CoNIC数据集上用作测试时增强方法时，它提升了细胞核实例分割和分类模型的性能。最后，我们将StainFuser应用于多千兆像素全切片图像，并在计算效率、图像质量以及跨切片一致性方面展现出优于当前方法的性能改进。