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- W4308833168 abstract "Confocal microscopy is the standard approach for obtaining volumetric images of a sample with high axial and lateral resolution, especially when dealing with scattering samples. Unfortunately, a confocal microscope is quite expensive compared to traditional microscopes. In addition, the point scanning in a confocal leads to slow imaging speed and photobleaching due to the high dose of laser energy. In this paper, we demonstrate how the advances in machine learning can be exploited to teach a traditional wide-field microscope, one that's available in every lab, into producing 3D volumetric images like a confocal. The key idea is to obtain multiple images with different focus settings using a wide-field microscope and use a 3D Generative Adversarial Network (GAN) based neural network to learn the mapping between the blurry low-contrast image stack obtained using wide-field and the sharp, high-contrast images obtained using a confocal. After training the network with widefield-confocal image pairs, the network can reliably and accurately reconstruct 3D volumetric images that rival confocal in terms of its lateral resolution, z-sectioning and image contrast. Our experimental results demonstrate generalization ability to handle unseen data, stability in the reconstruction results, high spatial resolution even when imaging thick ($sim40$ microns) highly-scattering samples. We believe that such learning-based-microscopes have the potential to bring confocal quality imaging to every lab that has a wide-field microscope." @default.
- W4308833168 created "2022-11-16" @default.
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- W4308833168 date "2021-10-14" @default.
- W4308833168 modified "2023-09-26" @default.
- W4308833168 title "Deep-3D Microscope: 3D volumetric microscopy of thick scattering samples using a wide-field microscope and machine learning" @default.
- W4308833168 doi "https://doi.org/10.48550/arxiv.2110.07218" @default.
- W4308833168 hasPublicationYear "2021" @default.
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