• Corpus ID: 64369848

Fast 3D cell tracking with wide-field fluorescence microscopy through deep learning

@article{Liu2018Fast3C,
  title={Fast 3D cell tracking with wide-field fluorescence microscopy through deep learning},
  author={Kan Liu and Hui Qiao and Jiamin Wu and Haoqian Wang and Lu Fang and Qionghai Dai},
  journal={arXiv: Optics},
  year={2018}
}
Tracking cells in 3D at high speed continues to attract extensive attention for many biomedical applications, such as monitoring immune cell migration and observing tumor metastasis in flowing blood vessels. Here, we propose a deep convolutional neural networks (CNNs) based method to retrieve the 3D locations of the fluorophores from a single 2D image captured by a conventional wide-field fluorescence microscope without any hardware modification. The reported method converts the challenging 3D… 

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References

SHOWING 1-10 OF 31 REFERENCES

Deep Learning Microscopy

It is demonstrated that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth of field, and can be used to design computational imagers that get better and better as they continue to image specimen and establish new transformations among different modes of imaging.

Compressive light-field microscopy for 3D neural activity recording

A new compressive light-field microscopy method that relies on spatial and temporal sparsity of fluorescence signals, allowing one to identify and localize each neuron in a 3D volume, with scattering and aberration effects naturally included and without ever reconstructing a volume image.

High density three-dimensional localization microscopy across large volumes

Lattice light-sheet microscopy is combined with newly developed, freely diffusing, cell-permeable chemical probes with targeted affinity for DNA, intracellular membranes or the plasma membrane to perform high–localization precision, ultrahigh–labeling density, multicolor localization microscopy in samples up to 20 μm thick.

Nanometer resolution imaging and tracking of fluorescent molecules with minimal photon fluxes

This work introduces MINFLUX, a concept for localizing photon emitters in space by probing the emitter with a local intensity minimum of excitation light, which minimizes the fluorescence photons needed for high localization precision.

Three-Dimensional Localization of Single Molecules for Super-Resolution Imaging and Single-Particle Tracking.

Single-molecule super-resolution fluorescence microscopy and single-particle tracking are two imaging modalities that illuminate the properties of cells and materials on spatial scales down to tens

Three-Dimensional Super-Resolution Imaging by Stochastic Optical Reconstruction Microscopy

3D stochastic optical reconstruction microscopy (STORM) is demonstrated by using optical astigmatism to determine both axial and lateral positions of individual fluorophores with nanometer accuracy, allowing the 3D morphology of nanoscopic cellular structures to be resolved.

Single shot, three-dimensional fluorescence microscopy with a spatially rotating point spread function.

A wide-field fluorescence microscope with a double-helix point spread function is constructed to obtain the specimen's three-dimensional distribution with a single snapshot and is suitable for studying the fast developing process of thin and sparsely distributed micron-scale cells in extended depth-of-field.

Volumetric Two-photon Imaging of Neurons Using Stereoscopy (vTwINS)

Two-photon laser scanning microscopy of calcium dynamics using fluorescent indicators is a widely used imaging method for large-scale recording of neural activity in vivo. Here, we introduce

Simultaneous whole-animal 3D-imaging of neuronal activity using light-field microscopy

This work demonstrates simultaneous functional imaging of neuronal activity at single-neuron resolution in an entire Caenorhabditis elegans and in larval zebrafish brain with high-speed volumetric calcium imaging.

Image Super-Resolution Using Deep Convolutional Networks

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep