Gutiérrez Pérez José Carlos, Otero Baguer Daniel, Maass Peter
Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany.
J Imaging. 2022 Jul 20;8(7):202. doi: 10.3390/jimaging8070202.
In recent years, numerous deep-learning approaches have been developed for the analysis of histopathology Whole Slide Images (WSI). A recurrent issue is the lack of generalization ability of a model that has been trained with images of one laboratory and then used to analyze images of a different laboratory. This occurs mainly due to the use of different scanners, laboratory procedures, and staining variations. This can produce strong color differences, which change not only the characteristics of the image, such as the contrast, brightness, and saturation, but also create more complex style variations. In this paper, we present a deep-learning solution based on contrastive learning to transfer from one staining style to another: StainCUT. This method eliminates the need to choose a reference frame and does not need paired images with different staining to learn the mapping between the stain distributions. Additionally, it does not rely on the CycleGAN approach, which makes the method efficient in terms of memory consumption and running time. We evaluate the model using two datasets that consist of the same specimens digitized with two different scanners. We also apply it as a preprocessing step for the semantic segmentation of metastases in lymph nodes. The model was trained on data from one of the laboratories and evaluated on data from another. The results validate the hypothesis that stain normalization indeed improves the performance of the model. Finally, we also investigate and compare the application of the stain normalization step during the training of the model and at inference.
近年来，已经开发了许多深度学习方法用于组织病理学全切片图像（WSI）的分析。一个反复出现的问题是，一个在一个实验室的图像上训练，然后用于分析另一个实验室图像的模型缺乏泛化能力。这种情况主要是由于使用了不同的扫描仪、实验室程序和染色差异。这会产生强烈的颜色差异，不仅会改变图像的特征，如对比度、亮度和饱和度，还会产生更复杂的风格变化。在本文中，我们提出了一种基于对比学习的深度学习解决方案，用于从一种染色风格转换到另一种染色风格：StainCUT。该方法无需选择参考帧，也不需要具有不同染色的配对图像来学习染色分布之间的映射。此外，它不依赖于CycleGAN方法，这使得该方法在内存消耗和运行时间方面都很高效。我们使用两个数据集对模型进行评估，这两个数据集由用两种不同扫描仪数字化的相同标本组成。我们还将其作为淋巴结转移语义分割的预处理步骤应用。该模型在其中一个实验室的数据上进行训练，并在另一个实验室的数据上进行评估。结果验证了染色归一化确实能提高模型性能的假设。最后，我们还研究并比较了染色归一化步骤在模型训练期间和推理时的应用。
