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.
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.
Chromatic aberrations can significantly diminish image quality when employing multiple wavelengths for imaging with a single optical system due to dispersion in both optical system and samples. To tackle this problem, we propose a novel approach using an adaptive achromatic lens, which is controlled by a trained Reinforcement Learning agent as part of a machine learning method. Notably, our method corrects chromatic aberrations prior to the imaging process, distinguishing it from conventional software-based post-processing approaches.
Conference Committee Involvement (1)
Optics and Photonics for Advanced Dimensional Metrology III
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