Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning

TL;DR

This paper enhances unsupervised stain-to-stain translation in digital pathology by integrating semantic guidance and extra channels into CycleGANs, improving translation quality for kidney histopathology structures.

Contribution

It introduces two novel methods—semantic guidance and extra channels—to improve stain translation effectiveness in digital pathology.

Findings

Semantic guidance improved instance-level segmentation accuracy.

Extra channels partially enhanced translation performance.

CycleGANs had limited success translating some structures.

Abstract

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.