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.
Digital inline holographic microscopy is a promising cellular object imaging modality. We report on two cost-efficient lens-less experimental set-ups comprising of a standard LED or a semiconductor laser light source with a Raspberry Pi Camera for image acquisition. The microscope parts are 3D-printed yielding a highly compact and portable microimaging solution. Tobacco cells, human red blood cells and polystyrene microspheres are successfully imaged by an open-source reconstruction software. The developed microscopes are cost-efficient (<$200) and yield spatial resolutions of 3.91 μm respectively 1.55 μm. They constitute a flexible tool for science and student and early researcher education that can be tailored to the researchers demand. All employed code is open-source accessible aiming at triggering further developments and sharing between research laboratories, diagnostic labs and science education.
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