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4 October 2024 Towards label-free imaging and analysis of 3D tissues using deep neural networks
Katharina Schmidt, Nektarios Koukourakis, Juergen W. Czarske
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Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC131180D (2024) https://doi.org/10.1117/12.3027004
Event: Nanoscience + Engineering, 2024, San Diego, California, United States
Abstract
Fluorescence microscopy is the gold standard for investigation of biological samples. While powerful, this technology has its limitations, including phototoxicity, technical difficulties in introducing fluorescent markers, and limited simultaneous labeling of different structures due to the need for spectral separation. Here, artificial intelligence-driven virtual staining can provide label-free imaging. Deep neural networks are trained to learn the correlation between a label-free image and a ground-truth fluorescence image. However, the main challenge for the transition from traditional to virtual staining is the acquisition of huge datasets including labeled and unlabeled data. We have performed proof-of-concept experiments to investigate the possibilities of transfer learning in virtual staining. Therefore, U-Net architectures were pretrained on a larger dataset and later on trained again on a smaller and more specific dataset. We show that transfer learning decreases the needed dataset size and may even improve prediction quality. Furthermore, the interpretability of the trained networks was studied.This was investigated using guided backpropagation and modified input images.
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Katharina Schmidt, Nektarios Koukourakis, and Juergen W. Czarske "Towards label-free imaging and analysis of 3D tissues using deep neural networks", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC131180D (4 October 2024); https://doi.org/10.1117/12.3027004
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KEYWORDS
Biological imaging
3D image processing
Nervous system
Biological research
Neural networks
Artificial neural networks
Biological samples
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