Presentation
19 June 2024 Virtual staining on unlabeled tissue samples
Author Affiliations +
Abstract
The gold standard in histology is to use chemical stains or genetic modified tissue, where some internal structures emit a fluorescent signal. These methods require trained staff and several hours or days of preparation. Virtual staining employs trained neural networks to take over the staining process. Based on an unlabeled microscopic images the network can predict the corresponding fluorescent image for DAPI and Phalloidin488 staining, enabling studies on cell nuclei and the cytoskeleton.

Neural networks usually need a huge amount of training data, so the possibilities of transfer learning for a reduction of the dataset size were investigated. In addition, we also present first studies the interpretability of the trained network to find ideal image acquisition techniques and optimize the training.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Katharina Schmidt, Ning Guo, Nektarios Koukourakis, Max von Witzleben, Michael Gelinsky, and Juergen W. Czarske "Virtual staining on unlabeled tissue samples", Proc. SPIE PC13011, Data Science for Photonics and Biophotonics, PC1301105 (19 June 2024); https://doi.org/10.1117/12.3016903
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