Browse
Submit Data
Databases
API
Help

Dataset Information

12 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Counting Small Particles in Electron Microscopy Images-Proposal for Rules and Their Application in Practice.

ABSTRACT: Electron microscopy (EM) is the gold standard for the characterisation of the morphology (size and shape) of nanoparticles. Visual observation of objects under examination is always a necessary first step in the characterisation process. Several questions arise when undertaking to identify and count particles to measure their size and shape distribution. In addition to challenges with the dispersion and identification of the particles, more than one protocol for counting particles is in use. This paper focuses on precise rules for the counting of particles in EM micrographs, as this influences the measurement accuracy of the number of particles, thus implicitly affecting the size values of the counted particles. We review and compare four different, commonly used methods for counting, which we then apply in case studies. The impact of the selected counting rule on the obtained final particle size distribution is highlighted. One main aim of this analysis is to support the application of a specific, well-defined counting approach in accordance with regulatory requirements to contribute to achieving more reliable and reproducible results. It is also useful for the new harmonised measurement procedures for determining the particle size and particle size distribution of nanomaterials.

SUBMITTER: Bresch H

PROVIDER: S-EPMC9268650 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Calculating small-angle scattering intensity functions from electron-microscopy images.

Project description:We outline procedures to calculate small-angle scattering (SAS) intensity functions from 2-dimensional electron-microscopy (EM) images. Two types of scattering systems were considered: (a) the sample is a set of particles confined to a plane; or (b) the sample is modelled as parallel, infinitely long cylinders that extend into the image plane. In each case, an EM image is segmented into particle instances and the background, whereby coordinates and morphological parameters are computed and used to calculate the constituents of the SAS-intensity function. We compare our results with experimental SAS data, discuss limitations, both general and case specific, and outline some applications of this method which could potentially complement experimental SAS.

| S-EPMC9169464 | biostudies-literature

Espina: a tool for the automated segmentation and counting of synapses in large stacks of electron microscopy images.

Project description:The synapses in the cerebral cortex can be classified into two main types, Gray's type I and type II, which correspond to asymmetric (mostly glutamatergic excitatory) and symmetric (inhibitory GABAergic) synapses, respectively. Hence, the quantification and identification of their different types and the proportions in which they are found, is extraordinarily important in terms of brain function. The ideal approach to calculate the number of synapses per unit volume is to analyze 3D samples reconstructed from serial sections. However, obtaining serial sections by transmission electron microscopy is an extremely time consuming and technically demanding task. Using focused ion beam/scanning electron microscope microscopy, we recently showed that virtually all synapses can be accurately identified as asymmetric or symmetric synapses when they are visualized, reconstructed, and quantified from large 3D tissue samples obtained in an automated manner. Nevertheless, the analysis, segmentation, and quantification of synapses is still a labor intensive procedure. Thus, novel solutions are currently necessary to deal with the large volume of data that is being generated by automated 3D electron microscopy. Accordingly, we have developed ESPINA, a software tool that performs the automated segmentation and counting of synapses in a reconstructed 3D volume of the cerebral cortex, and that greatly facilitates and accelerates these processes.

| S-EPMC3099746 | biostudies-literature

Electron counting detectors in scanning transmission electron microscopy via hardware signal processing.

Project description:Transmission electron microscopy is a pivotal instrument in materials and biological sciences due to its ability to provide local structural and spectroscopic information on a wide range of materials. However, the electron detectors used in scanning transmission electron microscopy are often unable to provide quantified information, that is the number of electrons impacting the detector, without exhaustive calibration and processing. This results in arbitrary signal values with slow response times that cannot be used for quantification or comparison to simulations. Here we demonstrate and optimise a hardware signal processing approach to augment electron detectors to perform single electron counting.

| S-EPMC10457289 | biostudies-literature

Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images.

Project description:Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30-200 nm) that function as conveyors of information between cells, reflecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two different state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.

| S-EPMC6744556 | biostudies-literature

Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks.

Project description:We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images "from scratch", without the need for large training data sets of manually annotated images. The whole process only requires about 15 min of hands-on time by a user and can typically be finished within less than 12 h when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.

| S-EPMC7925552 | biostudies-literature

Influence of electron dose rate on electron counting images recorded with the K2 camera.

Project description:A recent technological breakthrough in electron cryomicroscopy (cryoEM) is the development of direct electron detection cameras for data acquisition. By bypassing the traditional phosphor scintillator and fiber optic coupling, these cameras have greatly enhanced sensitivity and detective quantum efficiency (DQE). Of the three currently available commercial cameras, the Gatan K2 Summit was designed specifically for counting individual electron events. Counting further enhances the DQE, allows for practical doubling of detector resolution and eliminates noise arising from the variable deposition of energy by each primary electron. While counting has many advantages, undercounting of electrons happens when more than one electron strikes the same area of the detector within the analog readout period (coincidence loss), which influences image quality. In this work, we characterized the K2 Summit in electron counting mode, and studied the relationship of dose rate and coincidence loss and its influence on the quality of counted images. We found that coincidence loss reduces low frequency amplitudes but has no significant influence on the signal-to-noise ratio of the recorded image. It also has little influence on high frequency signals. Images of frozen hydrated archaeal 20S proteasome (~700 kDa, D7 symmetry) recorded at the optimal dose rate retained both high-resolution signal and low-resolution contrast and enabled calculating a 3.6 Å three-dimensional reconstruction from only 10,000 particles.

| S-EPMC3854003 | biostudies-literature

Spatial Transcriptomics correlated Electron Microscopy

Project description:This SuperSeries is composed of the SubSeries listed below.

2023-06-21 | GSE202638 | GEO

A small-dataset-trained deep learning framework for identifying atoms on transmission electron microscopy images.

Project description:To accurately identify atoms on noisy transmission electron microscope images, a deep learning (DL) approach is employed to estimate the map of probabilities at each pixel for being an atom with element discernment. Thanks to a delicately-designed loss function and the ability to extract features, the proposed DL networks can be trained by a small dataset created from approximately 30 experimental images, each with a size of 256 × 256 pixels2. The accuracy and robustness of the network were verified by resolving the structural defects of graphene and polar structures in PbTiO3/SrTiO3 multilayers from both the general TEM images and their imitated images on which intensities of some pixels lost randomly. Such a network has the potential to identify atoms from very few images of beam-sensitive material and explosive images recorded in a dynamical atomic process. The idea of using a small-dataset-trained DL framework to resolve a specific problem may prove instructive for practical DL applications in various fields.

| S-EPMC9929221 | biostudies-literature

Spatial Transcriptomics correlated Electron Microscopy [MERFISH]

Project description:Current spatial transcriptomics methods provide molecular and spatial information but no morphological readout. Here, we present STEM - a method that correlates multiplexed error-robust FISH with electron microscopy from neighboring tissue sections of the same sample. STEM links transcriptional and spatial organization of single cells with ultrastructural morphology of the tissue in vivo. Using STEM to characterize demyelinated white-matter lesions allowed us to link morphology of myelin-laden foamy microglia to transcriptional signature. Moreover, we revealed that interferon-response microglia have unique morphology and are enriched near CD8 T-cells.

2023-06-21 | GSE202623 | GEO

Fast principal component analysis for cryo-electron microscopy images.

Project description:Principal component analysis (PCA) plays an important role in the analysis of cryo-electron microscopy (cryo-EM) images for various tasks such as classification, denoising, compression, and ab initio modeling. We introduce a fast method for estimating a compressed representation of the 2-D covariance matrix of noisy cryo-EM projection images affected by radial point spread functions that enables fast PCA computation. Our method is based on a new algorithm for expanding images in the Fourier-Bessel basis (the harmonics on the disk), which provides a convenient way to handle the effect of the contrast transfer functions. For N images of size L × L, our method has time complexity O(NL3 + L4) and space complexity O(NL2 + L3). In contrast to previous work, these complexities are independent of the number of different contrast transfer functions of the images. We demonstrate our approach on synthetic and experimental data and show acceleration by factors of up to two orders of magnitude.

| S-EPMC10465116 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data