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The Scanning Electron Microscope (SEM) as 2D imaging instrument has been widely used in biological, mechanical, and materials sciences to determine the surface attributes (e.g., compositions or geometries) of microscopic specimens. A SEM offers an excellent capability to overcome the limitation of human eyes by achieving increased magnification, contrast, and resolution greater than 1 nanometer. However, SEM micrographs still remain two-dimensional (2D). Having truly three-dimensional (3D) shapes from SEM micrographs would provide anatomic surfaces allowing for quantitative measurements and informative visualization of the objects being investigated. In biology, for example, 3D SEM surface reconstructions would enable researchers to investigate surface characteristics and recognize roughness, flatness, and waviness of a biological structure. There are also various applications in material and mechanical engineering in which 3D representations of material properties would allow us to accurately measure a fractal dimension and surface roughness and design a micro article which needs to fit into a tiny appliance. 3D SEM surface reconstruction employs several computational technologies, such as multi-view geometry, computer vision, optimization strategies, and machine learning to tackle the inverse problem going from 2D to 3D. In this contribution, an attempt is made to provide a 3D microscopy dataset along with the underlying algorithms publicly and freely available at http://selibcv.org/3dsem/ for the research community. (2015-12-09)

Acknowledgements

https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HVBW0Q - Publication - Tafti, A.P., Kirkpatrick, A.B., Holz, J.D., Owen, H.A. and Yu, Z., 2015. 3DSEM: A 3D Microscopy Dataset. Data in Brief. doi: 10.1016/j.dib.2015.11.018

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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/

Supplementary Movies of the peer reviewed publication:FIB/SEM technology and high-throughput 3D reconstruction of dendritic spines and synapses in GFP-labeled adult-generated neurons. Front. Neuroanat. | doi: 10.3389/fnana2015.00060

Scanning electron microscopy (SEM) has an important application in the petroleum field, which is often used to analyze the microstructure of reservoir rocks, etc. Most of these analyses are based on two-dimensional images. In fact, SEM can carry out micro-nano scale three-dimensional measurement, and three-dimensional models can provide more accurate information than two-dimensional images. Among the commonly used SEM 3D reconstruction methods, parallax depth mapping is the most commonly used method. Multiple SEM images can be obtained by continuously tilting the sample table at a certain Angle, and multiple point clouds can be generated according to the parallax depth mapping method, and a more complete point clouds recovery can be achieved by combining the point clouds registration. However, the root mean square error of the point clouds generated by this method is relatively large and unstable after participating in point clouds registration. Therefore, this paper proposes a new method for generating point clouds. Firstly, the sample stage is rotated by a certain angle to obtain two SEM images. This operation makes the rotation matrix a known quantity. Then, based on the imaging model, an equation system is constructed to estimate the unknown translation parameters, and finally, triangulation is used to obtain the point clouds. The method proposed in this paper was tested on a publicly available 3D SEM image set, and the results showed that compared to the disparity depth mapping method, the point clouds generated by our method showed a significant reduction in root mean square error and relative rotation error in point clouds registration.

Materials qualification of reactor structural materials is a critical step in rapid implementation of advanced nuclear reactor technologies, particularly to assess the corrosion performance in these designs. Accelerated qualification of reactor structural materials requires incorporating powerful computational toolsets, such as phase field modelling in the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, to predict the evolution of structural materials due to corrosion. Accordingly, computational toolsets will require experimental data generated at appropriate length scales to validate accuracy. Focused ion beam (FIB) provides a high degree of control over manipulation of materials for analytical purposes, including capturing data on the evolution in the microstructure and elemental composition of materials at the mesoscale, an appropriate length scale for phase field modelling of intergranular diffusion phenomena using the MOOSE framework. For instance, the FEI Helios G4 UX dual beam plasma FIB microscope at the Irradiated Materials Characterization Laboratory (IMCL) is capable of backscatter diffraction (EBSD) and energy-dispersive x-ray spectroscopy (EDS) documenting the evolution in the microstructure and elemental composition, respectively. The Helios can perform EDS and EBSD three-dimensionally (3D) using tomography, which is then combined using different software packages to visualize 3D volumes correlating elemental composition to microstructural data. The purpose of this investigation was to develop a streamlined characterization and data processing workflow for 3D tomography studies on the FEI Helios G4 plasma FIB. The investigation is segmented into three parts: 1) Optimizing the data collection workflow, 2) identifying appropriate data processing and visualization software (i.e. DREAM.3D, MIPAR, and VGStudioMax), and 3) establishing an infrastructure for public release. The optimization of the data collection workflow is in collaboration with members of the U220 department to setup formal training on the tomography operation of the G4, through ThermoFisher Scientific, and exploring DREAM.3D, MIPAR, and VGStudioMax data processing/visualization software packages. VGStudioMax currently demonstrates the most promise for future use. Optimization of the data collection and processing workflow is still ongoing. A collaboration with INL High Performance Computing (HPC) established an open-source license for expediting the public release of FIB tomography datasets through HPC. FIB tomography data generated by the G4 will provide comprehensive data for validating 3D phase field mesoscale modelling tools within the MOOSE framework for accelerated qualification of reactor structural materials. label::after { content: "" !important; }

The dataset consists of scanning electron microscope (SEM) images of 3D-imprinted microneedles from fabricated conductive, UV-cured hydrogels composites Financing: Miniatura 7, DEC-2023/07/X/ST5/01377.

Synapses have been identified, segmented and quantified in the adult mouse hippocampus with 3D electron microscopy (FIB-SEM). Three animals have been used (ID2, ID4 and ID28). Three layers of the CA1 area have been studied: stratum oriens, stratum radiatum and stratum lacunosum-moleculare. Data obtained include the number of asymmetric and symmetric synapses, their sizes and their spatial distribution.

3D ultrastructure of myelinated and unmyelinated axons in a rat pelvic nerve visualized by serial block-face scanning electron microscopy (SBF-SEM)

We used Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM) to perform a 3D analysis of the synapses in the layer III neuropils of the Brodmann areas 3b (somatosensory), 4 (motor), and 17 (visual primary) from human brain samples. 3 human brain autopsies cases have been used to achieve a total of 22 FIB/SEM valid image stacks: 4 stacks in BA17 from a single case (AB7); 9 stacks in BA3b (three stacks per case, AB2, AB3, and AB7); and 9 stacks in BA4 (three stacks per case, AB2, AB3, and AB7). Specifically, we studied synaptic junctions, which were fully reconstructed in 3D. We analyzed the synaptic density, 3D spatial distribution, and type (excitatory and inhibitory), as well as the shape and size of each synaptic junction. Moreover, their postsynaptic targets were determined. The present dataset constitutes a detailed description of the synaptic characteristics of the human cortex, which is a necessary step to better understand the organization of the cortex.

A single Nickel nanowire has been characterised using 3 experimental techniques.Scanning electron microscope (SEM) data folder contains a single .TIFF image of a fallen Nickel nanowire, where the title refers to the name of the sample.Atomic and magnetic force micrscope (AFM and MFM) data folder contains raw output data where titles refer to the name of the sample (181017JA) and the magnetic field applied (eg 0mT), from software Nanoscope 5, these can be opened in any AFM processing software such as Gwyddion or WSxM. Each file contains data regarding the height (corresponding to AFM) and the phase (corresponding to the MFM).Simulation data folder contains .VTS files where the titles correspond to the appropriate field applied to the simulated wire. The file type .VTS can be opened and viewed within a 3D visualisation program such as Paraview. Research results based upon these data are published at https://doi.org/10.3390/nano10030429

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

Synapses have been identified, segmented and quantified in the CA1 field of the human hippocampus with 3D electron microscopy (FIB-SEM). Superficial pyramidal layer and stratum radiatum have been studied. 3 cases have been used (AB1, AB2, AB3) to achieve a total of 18 valid images stacks. Data include the number of asymmetric and symmetric synapses, their sizes and their spatial distribution.

Discover the booming Cryo-FIB-SEM market! Explore its $250 million valuation, 15% CAGR projection to 2033, key drivers, and leading players like JEOL and Thermo Fisher. Learn about market segmentation, regional analysis, and future growth opportunities in this detailed market analysis.

Sample: Double-innervated Meissner corpuscle from the forepaw of a 3-week-old C57BL/6J WT mouseSample Description: Across mammalian skin, structurally complex and diverse mechanosensory end organs respond to mechanical stimuli and enable our perception of dynamic, light touch. How forces act on morphologically dissimilar mechanosensory end organs of the skin to gate the requisite mechanotransduction channel Piezo2 and excite mechanosensory neurons is not understood. Here, we report high-resolution reconstructions of the hair follicle lanceolate complex, Meissner corpuscle, and Pacinian corpuscle and the subcellular distribution of Piezo2 within them. Across all three end organs, Piezo2 is restricted to the sensory axon membrane, including axon protrusions that extend from the axon body. These protrusions, which are numerous and elaborate extensively within the end organs, tether the axon to resident non-neuronal cells via adherens junctions. These findings support a unified model for dynamic touch in which mechanical stimuli stretch hundreds to thousands of axon protrusions across an end organ, opening proximal, axonal Piezo2 channels and exciting the neuron.This dataset contains manually proofread automatic segmentation of the FIB-SEM dataset in jrc_mus-meissner-corpuscle-2.Protocol: Samples were dissected and drop fixed in glutaraldehyde and paraformaldehyde, and then osmicated with osmium tetroxide and potassium ferrocyanide, followed by osmium tetroxide only. Samples were subsequently stained with uranyl acetate and samarium chloride. Samples were dehydrated with an ethanol series followed by anhydrous acetone, infiltrated with Durcupan resin, and cured at 60°C.Contributions: Sample provided by Annie Handler (Harvard Medical School/HHMI) and Qiyu Zhang (Harvard Medical School/HHMI), prepared for imaging by Song Pang (HHMI/Janelia, currently at Yale School of Medicine), imaged by Song Pang and C. Shan Xu (HHMI/Janelia, currently at Yale School of Medicine), post data registration by C. Shan Xu, global image alignment and processing by Annie Handler and Qiyu Zhang, automatic segmentation by Tri M. Nguyen (Harvard Medical School) under the supervision of Wei-Chung Allen Lee (Harvard Medical School), ground truth annotation by Rebecca Plumb, Brianna Sanchez, Karyl Ashjian, Aria Shotland, Bartianna Brown, Madiha Kabeer, Nusrat Africawala, Stuart Cattel, Annie Handler, and Qiyu Zhang (all Harvard Medical School/HHMI), and segmentation proofreading by Annie Handler, Qiyu Zhang, and Michael Nolan-Tamariz (Harvard Medical School/HHMI).Acquisition ID: jrc_mus-meissner-corpuscle-2Voxel size (nm): 6 x 6 x 6 (x, y, z)Data dimensions (µm): 74.1 x 55.1 x 69.6 (x, y, z)Scanning speed (MHz): 1Dataset URL: s3://janelia-cosem-datasets/jrc_mus-meissner-corpuscle-2/jrc_mus-meissner-corpuscle-2.zarr/recon-1/labels/inference/segmentations/EM DOI: https://doi.org/10.25378/janelia.23969106Visualization Website: https://openorganelle.janelia.org/datasets/jrc_mus-meissner-corpuscle-2Publication: Handler et al., 2023

Discover the booming Cryo-FIB-SEM market! This analysis reveals key trends, growth drivers (materials science, life sciences), leading companies (JEOL, Carl Zeiss, Thermo Fisher), and future projections to 2033. Learn how advancements in cryogenic microscopy are shaping this high-resolution imaging sector.

In this project I work on developing ways to use a FIB-SEM to create a 3D model of biological samples. The method can be used in several projects with Grøn Dyst angles and I here report on my work on imaging malaria infected blood cells which is essential for a deeper understanding of how the parasite might be targeted by medicine, and algae samples that are essential for ecotoxicologial studies and later will be used for algae species used in biomass and biofuel production.

Raw Scanning electron micoroscope (SEM) Images for publication Sikora P., Skibicki S., Chougan M., Szewczyk P., Cendrowski K., Federowicz K., El-Khayatt A.M., Saudi H.A., Strzałkowski J., Abd Elrahman M., Techman M., Sibera D., Chung S.Y. Silica-coated admixtures of bismuth and gadolinium oxides for 3D printed concrete applications: Rheology, hydration, strength, microstructure, and radiation shielding perspective. Construction and Building Materials (2025) 470, 140563, https://doi.org/10.1016/j.conbuildmat.2025.140563

The global Scanning Electron Microscope (SEM) market, valued at $3.718 billion in 2025, is projected to experience robust growth, driven by increasing demand across diverse sectors. The compound annual growth rate (CAGR) of 4.5% from 2025 to 2033 indicates a significant expansion, fueled primarily by advancements in SEM technology, leading to enhanced resolution, faster imaging speeds, and broader applications. Key drivers include the rising adoption of SEMs in life sciences for detailed cellular and sub-cellular analysis, material sciences for characterizing material properties at the nanoscale, and semiconductor industries for quality control and defect analysis. The increasing prevalence of nanotechnology research and development further fuels market growth. Furthermore, the continuous innovation in FIB-SEM technology, offering 3D imaging capabilities, expands the scope of applications across various research and industrial domains. Competition among leading manufacturers like Thermo Fisher Scientific, Hitachi High-Technologies Corporation, and Jeol Ltd. drives technological advancements and market expansion. Segmentation by application (Life Sciences, Material Sciences) and type (W-SEM, FEG-SEM, FIB-SEM) highlights distinct growth trajectories, with the FIB-SEM segment expected to witness faster expansion owing to its superior capabilities. Regional variations in market growth are anticipated, with North America and Asia Pacific (particularly China) expected to dominate the market due to high research spending and technological adoption. However, challenges such as high initial investment costs and the need for skilled personnel could potentially restrain market growth to some extent. The forecast period from 2025 to 2033 anticipates substantial market expansion based on the projected CAGR and ongoing technological developments. Continued miniaturization and improved sensitivity of SEMs are anticipated to further broaden their applications in fields like medical diagnostics, environmental monitoring, and forensic science. The integration of advanced analytical techniques with SEMs, such as energy-dispersive X-ray spectroscopy (EDS), will also contribute to market growth. Despite potential challenges, the overall market outlook for SEMs remains highly positive, driven by the intrinsic value of detailed nanoscale imaging across numerous scientific and industrial applications. Growth is expected across all regions, with particular strength in established markets and emerging economies showing increasing investment in research and development.

Sample: Guard hair follicle from the back of a 3-week-old C57BL/6J WT mouseSample Description: Across mammalian skin, structurally complex and diverse mechanosensory end organs respond to mechanical stimuli and enable our perception of dynamic, light touch. How forces act on morphologically dissimilar mechanosensory end organs of the skin to gate the requisite mechanotransduction channel Piezo2 and excite mechanosensory neurons is not understood. Here, we report high-resolution reconstructions of the hair follicle lanceolate complex, Meissner corpuscle, and Pacinian corpuscle and the subcellular distribution of Piezo2 within them. Across all three end organs, Piezo2 is restricted to the sensory axon membrane, including axon protrusions that extend from the axon body. These protrusions, which are numerous and elaborate extensively within the end organs, tether the axon to resident non-neuronal cells via adherens junctions. These findings support a unified model for dynamic touch in which mechanical stimuli stretch hundreds to thousands of axon protrusions across an end organ, opening proximal, axonal Piezo2 channels and exciting the neuron.This dataset contains manually proofread automatic segmentation of the FIB-SEM dataset in jrc_mus-guard-hair-follicle.Protocol: Samples were dissected and drop fixed in glutaraldehyde and paraformaldehyde, and then osmicated with osmium tetroxide and potassium ferrocyanide, followed by osmium tetroxide only. Samples were subsequently stained with uranyl acetate and samarium chloride. Samples were dehydrated with an ethanol series followed by anhydrous acetone, infiltrated with Durcupan resin, and cured at 60°C.Contributions: Sample provided by Annie Handler (Harvard Medical School/HHMI) and Qiyu Zhang (Harvard Medical School/HHMI), prepared for imaging by Song Pang (HHMI/Janelia, currently at Yale School of Medicine), imaged by Song Pang and C. Shan Xu (HHMI/Janelia, currently at Yale School of Medicine), post data registration by C. Shan Xu, global image alignment and processing by Annie Handler and Qiyu Zhang, automatic segmentation by Tri M. Nguyen (Harvard Medical School) under the supervision of Wei-Chung Allen Lee (Harvard Medical School), ground truth annotation by Rebecca Plumb, Brianna Sanchez, Karyl Ashjian, Aria Shotland, Bartianna Brown, Madiha Kabeer, Nusrat Africawala, Stuart Cattel, Annie Handler, and Qiyu Zhang (all Harvard Medical School/HHMI), and segmentation proofreading by Annie Handler, Qiyu Zhang, and Michael Nolan-Tamariz (Harvard Medical School/HHMI).Acquisition ID: jrc_mus-guard-hair-follicleVoxel size (nm): 6 x 6 x 6 (x, y, z)Data dimensions (µm): 88.1 x 95.9 x 77.6 (x, y, z)Scanning speed (MHz): 1Dataset URL (Redirect): https://data.janelia.org/HBfInEM DOI: https://doi.org/10.25378/janelia.23969052Visualization Website: https://openorganelle.janelia.org/datasets/jrc_mus-guard-hair-folliclePublication: Handler, et al., 2023