  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.
