A comparative evaluation of image-to-image translation methods for stain transfer in histopathology

TL;DR

This paper systematically compares twelve image-to-image translation methods, including traditional and GAN-based, for stain transfer in histopathology, evaluating their quality, suitability for tissue grading, and visual realism to guide method selection.

Contribution

It provides a comprehensive evaluation of 12 stain transfer methods, highlighting their strengths and weaknesses to inform future applications in histopathology.

Findings

GAN-based methods generally produce more realistic images

Traditional methods are faster but less accurate

Certain methods better support deep learning tissue grading

Abstract

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.