ArXiv Preprint
Virtual stain transfer is a promising area of research in Computational
Pathology, which has a great potential to alleviate important limitations when
applying deeplearningbased solutions such as lack of annotations and
sensitivity to a domain shift. However, in the literature, the majority of
virtual staining approaches are trained for a specific staining or stain
combination, and their extension to unseen stainings requires the acquisition
of additional data and training. In this paper, we propose HistoStarGAN, a
unified framework that performs stain transfer between multiple stainings,
stain normalisation and stain invariant segmentation, all in one inference of
the model. We demonstrate the generalisation abilities of the proposed solution
to perform diverse stain transfer and accurate stain invariant segmentation
over numerous unseen stainings, which is the first such demonstration in the
field. Moreover, the pre-trained HistoStar-GAN model can serve as a synthetic
data generator, which paves the way for the use of fully annotated synthetic
image data to improve the training of deep learning-based algorithms. To
illustrate the capabilities of our approach, as well as the potential risks in
the microscopy domain, inspired by applications in natural images, we generated
KidneyArtPathology, a fully annotated artificial image dataset for renal
pathology.
Jelica Vasiljević, Friedrich Feuerhake, Cédric Wemmert, Thomas Lampert
2022-10-18
