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- W2894616867 abstract "We propose a novel approach to implement efficient reconstruction for light field microscopy. This approach is based on convolutional neural network (CNN). By taking a two-dimensional light field raw image as the input, our model is able to output its corresponding three-dimensional volume at high resolution. Compared to the traditional light field reconstruction method, our approach dramatically reduces the computation time cost, and significantly improves the image quality (resolution). As furthermore combing this deep learning light field microscopy with selective light-sheet illumination, we can achieve high-contrast, high-resolution (~1.6μm) three-dimensional imaging of acting C.elegans at a speed of fifty volumes per second. We furthermore apply this technique to the interrogation of beating zebrafish heart, intoto visualizing the cardiovascular hemodynamics inside zebrafish embryo by rapid volumetric recording of beating myocardium and blood flow. Our method is demonstrated to be promising in a wide range of biomedical applications such as neuroscience and development, in which high-resolution, high-speed volumetric imaging is highly desired." @default.
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- W2894616867 date "2018-10-02" @default.
- W2894616867 modified "2023-09-24" @default.
- W2894616867 title "Deep learning light field microscopy for rapid four-dimensional imaging of behaving animals" @default.
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