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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.
Three-dimensional (3D) scanning transmission electron microscopy (STEM) has become one of the primary tools for analytical characterization in materials science and also finds increasing use in the life sciences. A number of different recording schemes exist for the acquisition of 3D data using STEM, each capturing different spatial frequencies and, thus, different information about the shape of a specimen. In this article, we present and compare different sampling approaches based on images with both large and small depth of field. We highlight the latest contribution to 3D data acquisition, the combined tilt, and focal series. This recording scheme combines the advantages of tilt series-based tomography with 3D data acquisition using a focal series and is particularly beneficial for imaging specimens with a thickness of 1 µm or greater.
We conducted a comparative study of three widely used algorithms for the detection of fiducial markers in electron microscopy images. The algorithms were applied to four datasets from different sources. For the purpose of obtaining comparable results, we introduced figures of merit and implemented all three algorithms in a unified code base to exclude software-specific differences. The application of the algorithms revealed that none of the three algorithms is superior to the others in all cases. This leads to the conclusion that the choice of a marker detection algorithm highly depends on the properties of the dataset to be analyzed, even within the narrowed domain of electron tomography.