A dynamic scanning method based on signal-statistics for scanning electron microscopy

Timischl, Felix

doi:10.1002/sca.21110

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…This is possible with a small reduction in image quality, if the images have structures aligned in the x - and y -direction. A first dynamic scanning method was introduced by Timischl (2014). The approach is based on adjusting the scanning speed to the number of detected electrons in that pixel.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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With the growing importance of three-dimensional and very large field of view imaging, acquisition time becomes a serious bottleneck. Additionally, dose reduction is of importance when imaging material like biological tissue that is sensitive to electron radiation. Random sparse scanning can be used in the combination with image reconstruction techniques to reduce the acquisition time or electron dose in scanning electron microscopy. In this study, we demonstrate a workflow that includes data acquisition on a scanning electron microscope, followed by a sparse image reconstruction based on compressive sensing or alternatively using neural networks. Neuron structures are automatically segmented from the reconstructed images using deep learning techniques. We show 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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…During conversion from a non-Cartesian grid to a Cartesian grid, the number of pixels doubles with only a small reduction in image quality, at least in images with structures aligned in x- and y-direction. Timischl 22 proposed a dynamic scanning method based on adjusting the scanning speed to the amount of detected electrons. In bright appearing sample areas, more electrons are detected, and the scanning speed can be increased.…”

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confidence: 99%

Feature Adaptive Sampling for Scanning Electron Microscopy

Dahmen

Engstler

Pauly

et al. 2016

Sci Rep

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A new method for the image acquisition in scanning electron microscopy (SEM) was introduced. The method used adaptively increased pixel-dwell times to improve the signal-to-noise ratio (SNR) in areas of high detail. In areas of low detail, the electron dose was reduced on a per pixel basis, and a-posteriori image processing techniques were applied to remove the resulting noise. The technique was realized by scanning the sample twice. The first, quick scan used small pixel-dwell times to generate a first, noisy image using a low electron dose. This image was analyzed automatically, and a software algorithm generated a sparse pattern of regions of the image that require additional sampling. A second scan generated a sparse image of only these regions, but using a highly increased electron dose. By applying a selective low-pass filter and combining both datasets, a single image was generated. The resulting image exhibited a factor of ≈3 better SNR than an image acquired with uniform sampling on a Cartesian grid and the same total acquisition time. This result implies that the required electron dose (or acquisition time) for the adaptive scanning method is a factor of ten lower than for uniform scanning.

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Making the most of your electrons: Challenges and opportunities in characterizing hybrid interfaces with STEM

Ribet

Murthy

Roth³

et al. 2021

Materials Today

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A dynamic scanning method based on signal-statistics for scanning electron microscopy

Cited by 4 publications

References 7 publications

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

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

Feature Adaptive Sampling for Scanning Electron Microscopy

Making the most of your electrons: Challenges and opportunities in characterizing hybrid interfaces with STEM

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