  In digital pathology, many image analysis tasks are challenged by the need
for large and time-consuming manual data annotations to cope with various
sources of variability in the image domain. Unsupervised domain adaptation
based on image-to-image translation is gaining importance in this field by
addressing variabilities without the manual overhead. Here, we tackle the
variation of different histological stains by unsupervised stain-to-stain
translation to enable a stain-independent applicability of a deep learning
segmentation model. We use CycleGANs for stain-to-stain translation in kidney
histopathology, and propose two novel approaches to improve translational
effectivity. First, we integrate a prior segmentation network into the CycleGAN
for a self-supervised, application-oriented optimization of translation through
semantic guidance, and second, we incorporate extra channels to the translation
output to implicitly separate artificial meta-information otherwise encoded for
tackling underdetermined reconstructions. The latter showed partially superior
performances to the unmodified CycleGAN, but the former performed best in all
stains providing instance-level Dice scores ranging between 78% and 92% for
most kidney structures, such as glomeruli, tubules, and veins. However,
CycleGANs showed only limited performance in the translation of other
structures, e.g. arteries. Our study also found somewhat lower performance for
all structures in all stains when compared to segmentation in the original
stain. Our study suggests that with current unsupervised technologies, it seems
unlikely to produce generally applicable simulated stains.
