An AI-based method for virtual staining of histopathological tissue samples has been developed by researchers who are part of the Nordic ABCAP consortium.
The novel method produces computational images that very closely resemble those produced by the actual chemical staining process. According to the team, virtual staining reduces both the chemical burden and manual work needed for sample processing while also enabling the use of the tissue for other purposes than the staining itself.
In addition, the virtual staining method requires no special hardware or infrastructure beyond a regular light microscopy and a suitable computer.
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“The results are very widely applicable. There are plenty of topics for follow-up research, and the computational methods can still be improved. However, we can already envision several application areas where virtual staining can have a major impact in histopathology,” says Pekka Ruusuvouri, who led the computational part of the study.
The study was conducted in two parts. The first part focused on optimizing the tissue sample processing and imaging steps. The second part focused on optimizing virtual staining based on generative adversarial neural networks.
The results of the study were published in Laboratory Investigation and Patterns.