The Scanning Electron Microscope (SEM) as 2D imaging instrument has been widely used in biology and material sciences to determine the surface attributes of microscopic specimens. However the SEM micrographs still remain 2D images. To effectively measure and visualize the surface properties, we need to restore the shape model from the SEM images. Having 3D surfaces from SEM images would provide true anatomic shape of microscopic objects which allow for quantitative measurements and informative visualization of the system being investigated.The 3D Microscopy Dataset which is provided here includes both 2D images and 3D reconstructed surfaces of several biological material samples. To get the data and know about "Term Of Usage", please visit http://selibcv.org/3dsem/

A simple and cost effective alternative for fabricating custom Scanning Electron Microscope (SEM) sample holders using 3D printers and conductive polylactic acid filament is presented. The flexibility of the 3D printing process allowed for the fabrication of sample holders with specific features that enable the high-resolution imaging of nanoelectrodes and nanopipettes. The precise value of the inner semi cone angle of the nanopipettes taper was extracted from the acquired images and used for calculating their radius using electrochemical methods. Because of the low electrical resistivity presented by the 3D printed holder, the imaging of non-conductive nanomaterials, such as alumina powder, was found to be possible. The fabrication time for each sample holder was under 30 minutes and the average cost was less than $0.50 per piece. Despite being quick and economical to fabricate, the sample holders were found to be sufficiently resistant, allowing for multiple uses of the same holder.

This dataset includes 3D scanning electron microscopy (SEM) images of a female mouse corpus callosum and MATLAB code for random walker (RaW) segmentations of myelinated axons. The code additionally characterizes inner axonal diameters and fiber orientation dispersion within segments of the intra-axonal spaces and myelin sheaths.

In the study, a female 8-week-old C57BL/6 mouse was perfused trans-cardiacally using a fixative solution. The genu of corpus callosum was later excised from the midsagittal slice of the dissected brain, and the tissue was sampled from the central region of the genu and was fixed in the same fixative solution. The tissue was then stained and an En Bloc lead staining was performed to enhance membrane contrast. The brain sample was dehydrated in alcohol and acetone, and embedded in Durcupan ACM resin. Then, the tissue sample was analyzed with SEM.

The SEM dataset includes 4 files:

• datac.nii (a stack of SEM data, 200 slices; resolution: 24nm by 24nm by 100nm; volume: 36μm by 48μm by 20μm)
• maskc.nii (foreground mask)
• myelin_mask.nii (myelin mask generated by pixel-wise classifier in ilastik)
• fibers.nii (intra-axonal space segmented by using random-walker-based approach)

This dataset contains simulated FIB tomography data of nanoporous/hierarchical nanoporous gold, synthetic FIB-SEM images of hierarchical nanoporous gold and segmentation results of real hierarchical nanoporous gold dataset. Abstract of the paper: Focused ion beam (FIB) tomography is a destructive technique used to collect three-dimensional (3D) structural information at a resolution of a few nanometers. For FIB tomography, a material is eroded in a layer-wise manner. After the erosion of each layer (whose thickness ranges on the nanometer scale), the current material surface is imaged by a scanning electron microscope. Especially for nanoporous materials, the reconstruction of the 3D microstructure of the material, from the information collected during FIB tomography, is impaired by the shine-through effect. This effect prevents a unique mapping between voxel intensity values and material phase (e.g., solid or void). It often substantially reduces the accuracy of conventional methods for image segmentation. Here we demonstrate how machine learning can be used to tackle this problem. A bottleneck in doing so is the availability of sufficient training data. To overcome this problem, we present a novel approach to generate synthetic FIB-SEM images using Monte Carlo simulations, which can be used as training data for machine learning. Based on this approach, we compare the performance of different machine learning architectures for segmenting FIB tomography data of nanoporous materials. We demonstrate that two-dimensional (2D) convolutional neural network (CNN) architectures with a group of adjacent slices as input data as well as 3D CNN perform best and can improve the segmentation performance by more than 100%.

Supplemental Material, sj-xls-1-tpx-10.1177_0192623320979908 for Digital 3D Topographic Microscopy: Bridging the Gaps Between Macroscopy, Microscopy and Scanning Electron Microscopy by Serge D. Rousselle in Toxicologic Pathology

Dataset of raw and image processed 3D Cryo-FIB SEM data recorded from Mallomonas cells.

Two separate files are included in this dataset. They are a compilation of multiple cross-sectional SEM inclusion analyses of the same area, for 2 samples. Both samples are from the same heat, one from the lade metallurgy furnace (LMF) and the other from the tundish. The SEM analysis was carried out at Carnegie Mellon University using a Thermo Fisher / FEI Aspex Explorer, at 10 kV accelerating voltage. The data is filtered to remove non-inclusions (pores and erroneous readings). For each sample, a specified area was marked and analyzed in the SEM. Then the surface was polished to remove several micrometers and reanalyzed again in the SEM. This process was reiterated to obtain multiple serial sections (i.e. 3D inclusion distribution). Five serial sections were analyzed for the LMF sample, and six for the tundish sample. Inclusions' z coordinate was calculated based on the amount of material removed between sections.

Measurements were taken between different faces of the test cubes.

For this dataset synapses have been identified, segmented and quantified in the deep part of the pyramidal layer, the stratum oriens and the stratum lacunosum-moleculare of the CA1 field of the human hippocampus with 3D electron microscopy (FIB-SEM). 5 human autopsies have been used to achieve a total of 15 valid images stacks (3 stacks per case) per investigated region. Data include the number and density per volume of asymmetric and symmetric synapses, their sizes and their 3D spatial position. The distribution of their post-synaptic targets has also been determined. This dataset contains additional data related to the dataset by Dominguez-Alvaro et al. (2020; doi: 10.25493/6HRE-F2Y). Experiments were performed identically, and the data from these two dataset can be used in combination.

Synapses have been identified, segmented and quantified in the human neocortex (trans entorhinal cortex, TEC) with 3D electron microscopy (FIB-SEM). Layer II of the TEC has been studied. 5 cases have been used (AB1, AB2, IF10, M16 and M17) to achieve a total of 15 valid images stacks. Data obtained include the number of asymmetric and symmetric synapses, their sizes and their spatial distribution. The distribution of their post-synaptic targets has also been determined.

Data associated with paper published in Bone.

Varicella-zoster virus (VZV) is a human alphaherpesvirus that causes varicella (chickenpox) and herpes zoster (shingles). Like all herpesviruses, the VZV DNA genome is replicated in the nucleus and packaged into nucleocapsids that must egress across the nuclear membrane for incorporation into virus particles in the cytoplasm. Our recent work showed that VZV nucleocapsids are sequestered in nuclear cages formed from promyelocytic leukemia protein (PML) in vitro and in human dorsal root ganglia and skin xenografts in vivo. We sought a method to determine the three-dimensional (3D) distribution of nucleocapsids in the nuclei of herpesvirus-infected cells as well as the 3D shape, volume and ultrastructure of these unique PML subnuclear domains. Here we report the development of a novel 3D imaging and reconstruction strategy that we term Serial Section Array-Scanning Electron Microscopy (SSA-SEM) and its application to the analysis of VZV-infected cells and these nuclear PML cages. We show that SSA-SEM permits large volume imaging and 3D reconstruction at a resolution sufficient to localize, count and distinguish different types of VZV nucleocapsids and to visualize complete PML cages. This method allowed a quantitative determination of how many nucleocapsids can be sequestered within individual PML cages (sequestration capacity), what proportion of nucleocapsids are entrapped in single nuclei (sequestration efficiency) and revealed the ultrastructural detail of the PML cages. More than 98% of all nucleocapsids in reconstructed nuclear volumes were contained in PML cages and single PML cages sequestered up to 2,780 nucleocapsids, which were shown by electron tomography to be embedded and cross-linked by an filamentous electron-dense meshwork within these unique subnuclear domains. This SSA-SEM analysis extends our recent characterization of PML cages and provides a proof of concept for this new strategy to investigate events during virion assembly at the single cell level.

Focused ion beam (FIB) tomography is a destructive technique used to collect three-dimensional (3D) structural information at a resolution of a few nanometers. For FIB tomography, a material sample is degraded by layer-wise milling. After each layer, the current surface is imaged by a scanning electron microscope (SEM), providing a consecutive series of cross-sections of the three-dimensional material sample. Especially for nanoporous materials, the reconstruction of the 3D microstructure of the material, from the information collected during FIB tomography, is impaired by the so-called shine-through effect. This effect prevents a unique mapping between voxel intensity values and material phase (e.g., solid or void). It often substantially reduces the accuracy of conventional methods for image segmentation. Here we demonstrate how machine learning can be used to tackle this problem. A bottleneck in doing so is the availability of sufficient training data. To overcome this problem, we present a novel approach to generate synthetic training data in the form of FIB-SEM images generated by Monte Carlo simulations. Based on this approach, we compare the performance of different machine learning architectures for segmenting FIB tomography data of nanoporous materials. We demonstrate that two-dimensional (2D) convolutional neural network (CNN) architectures processing a group of adjacent slices as input data as well as 3D CNN perform best and can enhance the segmentation performance significantly.

Understanding cellular architecture is essential for understanding biology. Electron microscopy (EM) uniquely visualizes cellular structure with nanometer resolution. However, traditional methods, such as thin-section EM or EM tomography, have limitations inasmuch as they only visualize a single slice or a relatively small volume of the cell, respectively. Here, we overcome these limitations by long-term imaging whole cells and tissues via the enhanced Focus Ion Beam Scanning Electron Microscopy (FIB-SEM) platform in high resolution mode with month-long acquisition duration. We use this approach to generate reference 3D image data sets at 4-nm isotropic voxels. Together with subsequent segmentation, we hope to create a reference library to explore comprehensive quantification of whole cells and all their constituents, thus addressing questions related to cell identities, cell morphologies, cell-cell interactions, as well as intracellular organelle organization and structure. Fan-shaped body, the largest substructure of the central complex controls various behaviors of insects. To understand brain functions, knowing neuron connectivity at synapse level is critical. High-resolution FIB-SEM images with large volume provide significant values in both identifying synaptic structures and tracing fine neuronal profiles. The detailed examination of synapses and unique intracellular features could add important insights to the understanding of the types of synaptic vesicles, synaptic polarity and neuron’s property.Sample: Fan-shaped body of a 5 day-old adult male Drosophila (Genome type: iso Canton S G1 x w1118 iso 5905).Protocol: Chemical Fixation, ORTO-Lead-EPTA staining via progressive lowering of temperature and low temperature staining (PLT-LTS) heavy metal enhancement protocol.Contributions: Sample provided by Zhiyuan Lu (HHMI/Janelia), prepared, imaged and post-processed by Song Pang (HHMI/Janelia), with post-processing by C. Shan Xu (HHMI/Janelia).Dataset ID: jrc_fly-fsb-1Final voxel size (nm): 4.00 x 4.00 x 4.00 (X, Y, Z)Dimensions (µm): 45 x 56 x 45 (X, Y, Z)Acquisition date: 2019-09-14Dataset URL: https://data.janelia.org/TrW8bVisualization Website: https://openorganelle.janelia.org/datasets/jrc_fly-fsb-1Publication: “Isotropic 3D electron microscopy reference library of whole cells and tissues” by C. Shan Xu et al. (in preparation)

Photo images, 3D/CT data, mtDNA data, SEM images, and EDS elemental analysis of freshwater mussels that inhabit the Kyushu and Ryukyu Islands (61 individuals, nine species/subspecies) were published online in a local database (http://ffish.asia/Unionidae3D), GBIF (http://ipt.pensoft.net/resource?r=unionidae3d) and DDBJ/EMBL/Genbank (LC431810–LC431840).

A set of serial-section electron backscatter diffraction (EBSD) data files and a 3D reconstruction of a model Zircaloy-4 with 7 wt.% Nb addition alloy following hot-rolling.

The 3D data set contains measurements of the material rolled at 725C to 75% reduction. The measurements include indexing of both the alpha and the beta phases in 441 sequential slices, each of 0.1 μm, through a small section of the material, taken using the dual beam Thermo Scientific Helios Xe⁺ plasma focused ion-beam scanning electron microscope (PFIB-SEM). The 3D EBSD data set includes EBSD measurements in the form of ctf files, binary data files, an Aztec project file, and accompanying images for each slice.

A 3D volume was reconstructed from the 3D EBSD data set using a customised pipeline within the DREAM.3D software. The results of this analysis are included in the 'dream3d' folder, which includes a description of the pipeline, the final fully reconstructed dream3d data file, an xdmf file used for visualising the data in ParaView, a h5ebsd file containing results for the reconstruction, and grain averaged data for each of the identified features.

Please see our accompanying paper for analysis of the 3D reconstruction - as well as analysis of the 2D measurements from 10.5281/zenodo.3784460 - and for interpretation of the coupled crystallographic texture evolution;

C.S. Daniel, A. Garner, P.D. Honniball, L. Bradley, M. Preuss, P.B. Prangnell, J. Quinta da Fonseca, Co-deformation and dynamic annealing effects on the texture development during alpha–beta processing of a model Zr-Nb alloy, Acta Materialia 205 (2021) 116538. 10.1016/j.actamat.2020.116538

Fig. 11. Zospeum njunjicae Jochum, Schilthuizen & Ruthensteiner sp. nov. A–B. Light microscopic images of paratypes (NMBE 572616) showing aperture and dorsal views. C. Light microscopic images of holotype (NMBE 572617) showing aperture and dorsal views. D–I. 3D visualizations of X-ray microCT data of holotype (NMBE 572617). D. Aperture view. E. Aperture facing right view showing well defined lamella. F. Apical view. G. Dorsal view. H. Aperture facing left view. I. Ventral view.

Fig. 16. Light microscopic images of full-bodied Zospeum Bourguignat, 1856 from Njeguši, St John's cave. A–B. Zospeum kolbae Jochum, Inäbnit, Kneubühler & Ruthensteiner sp. nov. (NMBE 571122– 571123), individuals assessed by DNA sequencing with sigmoid intestine showing through shells (shells were destroyed post imaging for tissue extraction). A. Holotype (NMBE 571122), shell of aliquot, aperture, and aperture facing left view. B. Paratype (NMBE 571123), shell of aliquot, aperture, and aperture facing left view. C. Undescribed Zospeum sp. 1 (NMBE 577052) showing all perspectives. D–E. Undescribed Zospeum sp. 1 (NMBE 577053/2) showing all perspectives.

Fig. 6. Zospeum intermedium Jochum & Ruthensteiner sp. nov. (RMNH.MOL.234132) from Gittenberger (1975). A. Light microscopic images of apertural and dorsal views. B. Sample labels. C–H. 3D visualizations of X-ray Micro-CT data. C. Aperture view. D. Aperture facing right view. E. Apical view. F. Dorsal view. G. Aperture facing right view. H. Umbilical view showing slight umbilical depression and lamella projecting from the columellar side.