组织病理学中用于染色转移的图像到图像翻译方法的比较评估
A comparative evaluation of image-to-image translation methods for stain transfer in histopathology
AI总结：
本文比较了十二种用于组织病理学染色转移的图像到图像翻译方法，通过定量指标、深度学习分级适用性及病理学家视觉评估揭示各方法优劣，为合理选择I2I算法提供依据。
摘要：
图像到图像翻译（I2I）方法允许生成与原始图像共享内容但具有不同风格的人工图像。随着基于生成对抗网络（GANs）的方法的进步，I2I方法能够生成与自然图像难以区分的人工图像。最近，I2I方法也被应用于组织病理学，用于从不同类型的染色中生成计算机模拟染色组织的人工图像。我们将此过程称为染色转移。I2I变体的数量不断增加，这使得为染色转移选择最合适的I2I方法变得具有挑战性。在我们的工作中，我们比较了十二种染色转移方法，其中三种基于传统图像处理方法，九种基于GAN的图像处理方法。该分析依赖于图像翻译质量的互补定量指标、对基于深度学习的组织分级适用性的评估，以及病理学家的视觉评估。我们的研究突出了染色转移方法的优势和劣势，从而允许对底层I2I算法进行合理选择。用于H&E和Masson's Trichrome染色之间染色转移的代码、数据和训练模型将在线提供。
英文摘要：
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.
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