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Follow on Google News | Virtual staining of unlabeled autopsy tissue using AIBy: ucla ita In a recent work published in Nature Communications, a UCLA research team led by Professor Aydogan Ozcan developed an AI-powered computational staining approach for label-free autopsy tissue samples, which digitally transforms autofluorescence microscopic images of autopsy tissue sections into their high-quality hematoxylin and eosin (H&E) stained counterparts using a trained neural network model. This virtual staining method was proven to mitigate autolysis-induced artifacts inherent in traditional chemical staining of autopsy samples. It revealed the nuclear, cytoplasmic, and extracellular features clearly, particularly in tissue samples severely affected by autolysis, such as COVID-19 samples, where notable delays in tissue preservation occurred. To further demonstrate the effectiveness of this autopsy virtual staining technique and ensure its superior performance, four board-certified pathologists blindly evaluated the staining quality of test images from autopsy samples without knowing which image was virtually stained or chemically stained. They confirmed that there was no significant difference between the virtually stained and chemically stained H&E images for the well-preserved autopsy tissue regions in terms of nuclear detail, cytoplasmic detail, and extracellular detail, underscoring the efficacy of this technique. In addition to mitigating autolysis-induced artifacts observed in traditional chemical staining of autopsy samples, this virtual staining technique is also faster, more repeatable and cost-effective, eliminating toxic reagents and manual labor involved in the traditional staining workflow, and therefore holds substantial potential for widespread applications in histology field. Article: https://www.nature.com/ End
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