Deep learning microscopy
Yair Rivenson,Zoltán Göröcs,Harun Gunaydin,Yibo Zhang,Hongda Wang,Aydogan Ozcan +5 more
- Vol. 4, Iss: 11, pp 1437-1443
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TLDR
In this paper, a deep neural network was used to improve optical microscopy, enhancing its spatial resolution over a large field of view and depth of field. But, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design.Abstract:
We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field of view and depth of field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with better resolution, matching the performance of higher numerical aperture lenses and also significantly surpassing their limited field of view and depth of field. These results are significant for various fields that use microscopy tools, including, e.g., life sciences, where optical microscopy is considered as one of the most widely used and deployed techniques. Beyond such applications, the presented approach might be applicable to other imaging modalities, also spanning different parts of the electromagnetic spectrum, and can be used to design computational imagers that get better as they continue to image specimens and establish new transformations among different modes of imaging.read more
Citations
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All-optical machine learning using diffractive deep neural networks
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Content-aware image restoration: pushing the limits of fluorescence microscopy.
Martin Weigert,Uwe Schmidt,Tobias Boothe,Andreas Müller,Alexandr Dibrov,Akanksha Jain,Benjamin Wilhelm,Deborah Schmidt,Coleman Broaddus,Siân Culley,Siân Culley,Mauricio Rocha-Martins,Fabián Segovia-Miranda,Caren Norden,Ricardo Henriques,Ricardo Henriques,Marino Zerial,Michele Solimena,Jochen C. Rink,Pavel Tomancak,Loic Royer,Florian Jug,Eugene W. Myers,Eugene W. Myers +23 more
TL;DR: This work shows how content-aware image restoration based on deep learning extends the range of biological phenomena observable by microscopy by bypassing the trade-offs between imaging speed, resolution, and maximal light exposure that limit fluorescence imaging to enable discovery.
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Phase recovery and holographic image reconstruction using deep learning in neural networks
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Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials.
Wei Ma,Feng Cheng,Yongmin Liu +2 more
TL;DR: A deep-learning-based model is reported, comprising two bidirectional neural networks assembled by a partial stacking strategy, to automatically design and optimize three-dimensional chiral metamaterials with strong chiroptical responses at predesignated wavelengths.
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