SmartEM: machine-learning guided electron microscopy.

Connectomics provides essential nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole circuit or brain reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis.

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

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.

A bimodal tomographic reconstruction technique combining EDS-STEM and HAADF-STEM.

A three-dimensional (3D) chemical characterization of nanomaterials can be obtained using tomography based on high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) or energy dispersive X-ray spectroscopy (EDS) STEM. These two complementary techniques have both advantages and disadvantages. The Z-contrast images have good image quality but lack robustness in the compositional analysis, while the elemental maps give more element-specific information, but at a low signal-to-noise ratio and a longer exposure time.

MRC2014: Extensions to the MRC format header for electron cryo-microscopy and tomography.

The MRC binary file format is widely used in the three-dimensional electron microscopy field for storing image and volume data. Files contain a header which describes the kind of data held, together with other important metadata. In response to advances in electron microscopy techniques, a number of variants to the file format have emerged which contain useful additional data, but which limit interoperability between different software packages.

A fast algorithm for computing and correcting the CTF for tilted, thick specimens in TEM.

Today, the resolution in phase-contrast cryo-electron tomography is for a significant part limited by the contrast transfer function (CTF) of the microscope. The CTF is a function of defocus and thus varies spatially as a result of the tilting of the specimen and the finite specimen thickness. Models that include spatial dependencies have not been adopted in daily practice because of their high computational complexity.

Recent advances in electron tomography: TEM and HAADF-STEM tomography for materials science and semiconductor applications.

Electron tomography is a well-established technique for three-dimensional structure determination of (almost) amorphous specimens in life sciences applications. With the recent advances in nanotechnology and the semiconductor industry, there is also an increasing need for high-resolution three-dimensional (3D) structural information in physical sciences. In this article, we evaluate the capabilities and limitations of transmission electron microscopy (TEM) and high-angle-annular-dark-field scanning transmission electron microscopy (HAADF-STEM) tomography for the 3D structural characterization of partially crystalline to highly crystalline materials.