Abstract
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.
Abstract (translated)
污渍归一化算法旨在将源多兆像素 histology图像的色和强度特性转化为与目标图像相同的特征，从而减轻在图像中使用污渍突出细胞组分时出现的 inconsistencies。我们提出了一种新方法StainFuser，将其视为一种风格迁移任务，利用新颖的条件随机场架构解决此问题，无需手动创建颜色组件。通过这种方法，我们curate SPI-2M，至今最大的污渍归一化数据集，为超过200万张历史图像提供神经风格迁移高质变换。在训练此数据的基础上，StainFuser在高品质图像变换方面优于当前的 GAN 和手工方法。此外，与现有方法相比，它改进了用于挑战性的 CoNIC 数据集作为测试时间扩充方法时对核实例分割和分类模型的性能。最后，我们将 StainFuser 应用于多兆像素 whole slide images (WSIs)，并在计算效率、图像质量和贴图中的一致性方面展示出改进。与现有方法相比，StainFuser 在此方面的表现更加卓越。
URL
https://arxiv.org/abs/2403.09302
