Image-to-image translation (I2I) methods allow the generation of artificial images that share the content of the original image but have a different style. With the advances in Generative Adversarial Networks (GANs)-based methods, I2I methods enabled the generation of artificial images that are indistinguishable from natural images. Recently, I2I methods were also employed in histopathology for generating artificial images of in silico stained tissues from a different type of staining. We refer to this process as stain transfer. The number of I2I variants is constantly increasing, which makes a well justified choice of the most suitable I2I methods for stain transfer challenging. In our work, we compare twelve stain transfer approaches, three of which are based on traditional and nine on GAN-based image processing methods. The analysis relies on complementary quantitative measures for the quality of image translation, the assessment of the suitability for deep learning-based tissue grading, and the visual evaluation by pathologists. Our study highlights the strengths and weaknesses of the stain transfer approaches, thereby allowing a rational choice of the underlying I2I algorithms. Code, data, and trained models for stain transfer between H&E and Masson's Trichrome staining will be made available online.

翻译：图像到图像转换（I2I）方法能够生成与原始图像内容一致但风格不同的人工图像。随着基于生成对抗网络（GANs）方法的进步，I2I方法已能够生成与自然图像无法区分的人工图像。近年来，I2I方法也被应用于组织病理学中，用于从一种染色类型生成另一种染色类型的计算机模拟染色组织人工图像。我们将此过程称为染色转换。由于I2I变体数量不断增加，如何合理选择最适用于染色转换的I2I方法成为一项挑战。在我们的研究中，我们比较了十二种染色转换方法，其中三种基于传统图像处理方法，九种基于GAN方法。分析依赖于图像转换质量的互补定量指标、基于深度学习的组织分级适用性评估以及病理学家的视觉评估。我们的研究揭示了染色转换方法的优势与不足，从而为合理选择底层I2I算法提供了依据。H&E染色与Masson三色染色之间染色转换的代码、数据和训练模型将在线公开。