Learning Guided Electron Microscopy with Active Acquisition

@inproceedings{Mi2020LearningGE,
  title={Learning Guided Electron Microscopy with Active Acquisition},
  author={Lu Mi and Hao Wang and Yaron Meirovitch and Richard Lee Schalek and Srinivas C. Turaga and Jeff William Lichtman and Aravinthan D. T. Samuel and Nir Shavit},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2020}
}
Single-beam scanning electron microscopes (SEM) are widely used to acquire massive datasets for biomedical study, material analysis, and fabrication inspection. Datasets are typically acquired with uniform acquisition: applying the electron beam with the same power and duration to all image pixels, even if there is great variety in the pixels’ importance for eventual use. Many SEMs are now able to move the beam to any pixel in the field of view without delay, enabling them, in principle, to… 
1 Citations

Deep Learning-Based Point-Scanning Super-Resolution Imaging

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References

SHOWING 1-10 OF 30 REFERENCES

Sparse Scanning Electron Microscopy Data Acquisition and Deep Neural Networks for Automated Segmentation in Connectomics

It is shown that the average dwell time per pixel can be reduced by a factor of 2–3, thereby providing a real-life confirmation of previous results on simulated data in one of the key segmentation applications in connectomics and thus demonstrating the feasibility and benefit of random sparse scanning techniques for a specific real-world scenario.

Sparse imaging for fast electron microscopy

This work proposes and demonstrates on an operational SEM a fast method to sparsely sample and reconstruct smooth images, and reports image fidelity as a function of acquisition speed by comparing traditional raster to sparse imaging modes.

Feature Adaptive Sampling for Scanning Electron Microscopy

A new method for the image acquisition in scanning electron microscopy (SEM) was introduced that adaptively increased pixel-dwell times to improve the signal-to-noise ratio (SNR) in areas of high detail and that implies the required electron dose for the adaptive scanning method is a factor of ten lower than for uniform scanning.

Scanning Transmission Electron Microscopy

  • P. Nellist
  • Physics
    Springer Handbook of Microscopy
  • 2019
The scanning transmission electron microscope () has become one of the preeminent instruments for high spatial resolution imaging and spectroscopy of materials, most notably at atomic resolution. The

Deep Learning-Based Point-Scanning Super-Resolution Imaging

It is shown that undersampled confocal images combined with a multiframe PSSR model trained on Airyscan timelapses facilitatesAiryscan-equivalent spatial resolution and SNR with ~100x lower laser dose and 16x higher frame rates than corresponding high-resolution acquisitions.

Deep learning enables cross-modality super-resolution in fluorescence microscopy

This data-driven approach does not require numerical modeling of the imaging process or the estimation of a point-spread-function, and is based on training a generative adversarial network to transform diffraction-limited input images into super-resolved ones, and could serve to democratize super-resolution imaging.

Content-aware image restoration: pushing the limits of fluorescence microscopy

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.

High-resolution, high-throughput imaging with a multibeam scanning electron microscope

This work uses multiple electron beams in a single column and detect secondary electrons in parallel to increase the imaging speed by close to two orders of magnitude and demonstrates imaging for a variety of samples ranging from biological brain tissue to semiconductor wafers.

Scanning transmission electron microscopy *

  • A. Crewe
  • Physics
    Journal of microscopy
  • 1974
The conditions necessary for the achievement of high resolution and also the various modes of contrast which can be obtained from the scanning transmission electron microscope are examined.