We present LodeSTAR, an unsupervised, single-shot object detector for microscopy. LodeSTAR exploits the symmetries of problem statements to train neural networks using extremely small datasets and without ground truth. We demonstrate that LodeSTAR is comparable to state-of-the-art, supervised deep learning methods, despite training on orders of magnitude less training data, and no annotations. Moreover, we demonstrate that LodeSTAR achieves near theoretically optimal results in terms of sub-pixel positioning of objects of various shapes. Finally, we show that LodeSTAR can exploit additional symmetries to measure additional particle properties, such as the axial position of objects and particle polarizability.
We demonstrate a new technique that combines holographic microscopy and deep learning to track microplankton through multiple generations, and measure their 3D positions and dry mass. The method is minimally invasive and non-destructive to the plankton cells, allowing us to study their trophic interactions, feeding events, and bio mass increase throughout the cell cycle. We evaluate the method on various plankton species belonging to different trophic levels, and observe the dry mass transfer during feeding interactions and diatom growth dynamics. Our approach provides a valuable tool for understanding microplankton behaviour and interactions in the oceanic food web.
We present a technique to track microplanktons through generations, and continuously measure their three-dimensional position and dry mass. By combining holographic microscopy with deep learning, the technique is minimally invasive and non-destructive for plankton cells, allowing quantitative assessments of trophic interactions such as feeding events, biomass increase throughout the cell cycle. We evaluate the performance of the method, by applying it to various plankton species belonging to different trophic levels. Finally, we demonstrate the dry mass transfer from cell to cell in prey-predator interactions, and show the growth dynamics from division to division in diatoms.
Phytoplankton are responsible for approximately 50% of the biological fixation of carbon dioxide and oxygen production on Earth. The majority of the phytoplankton production is consumed by single-celled microscopic grazers, microzooplankton. In this experiment, we reproduce a small-scale alias of the plankton world to understand their feeding behavior. We use a lens-less holographic approach, driven by deep learning powered DeepTrack 2.0. We use a combination of U-net and CNN architectures to decipher the properties of radius, refractive index, heights and drymass of plankton. We further compare the results with standard approaches.
Digital Holographic Microscopy (DHM) has been a successful imaging technique for various applications in biomedical imaging, particle analysis, and optical engineering. Though DHM has been successful in reconstructing 3D volumes with stationary objects, it has still been a challenging task to track fast mobile objects. Recent advancements in deep learning with convolutional neural networks have been proven useful in solving experimental difficulties, starting from tracking single particles to multiple bacterial cells. Here, we propose a compact DHM driven by neural networks with a minimal amount of optical elements with an ultimate aim for easy usage and transportation.
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