Virtual staining in digital pathology: GAN-based stain virtualization of whole slide images

Abstract

Histopathological evaluation is central to diagnosis and drug development, especially in oncology. Beyond the standard H&E stain, target-specific stains such as immunohistochemistry (IHC) are often needed — but tissue is scarce, staining is slow and costly, and every additional stain consumes another tissue section. Cancer-immunotherapy biomarker research adds the burden of manually annotating multistain slides.

What we did

• Segmenting multistain slides from one shot. A fully convolutional network with one-shot colour deconvolution segments digitised slides with multiple stainings into tumour, healthy tissue, necrosis and background, generalising across immunohistochemistry stains to reduce pathologists’ annotation workload (IET Image Processing 2019).
• Virtualizing the stain. Unsupervised style-transfer GANs generate realistic, clinically meaningful images of stained tissue — for example FAP-CK immunofluorescence from other stainings — opening the way to in-silico multiplexing of several stains on the same tissue section (European Congress on Digital Pathology 2019).
• Seamless at gigapixel scale. Whole slide images must be processed tile by tile, and instance normalization leaves visible tiling artifacts when the tiles are stitched back together. A perceptual embedding consistency loss removes these artifacts (MICCAI 2019), and the full framework was validated for seamless virtual whole-slide synthesis (IEEE JBHI 2020).

Our role

Scientific lead as a postdoc at the Chair for Computer Aided Medical Procedures (TU Munich, PI: Nassir Navab), and co-supervision — with Eldad Klaiman (Roche Diagnostics, Penzberg) — of Amal Lahiani’s industrial PhD (2016–2020).

Perspective

The project showed that virtual staining can be made clinically interpretable and seamless at whole-slide scale, and seeded our later work on trustworthy digital staining in hematology.