scholarly journals A guide to analysis and reconstruction of serial block face scanning electron microscopy data

2018 ◽  
Vol 270 (2) ◽  
pp. 217-234 ◽  
Author(s):  
E. COCKS ◽  
M. TAGGART ◽  
F.C. RIND ◽  
K. WHITE
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Scanning Electron Microscopy Data ◽  
Electron Microscopy Data ◽  
Face Scanning ◽  
Block Face ◽  
Microscopy Data ◽  
Scanning Electron

scholarly journals Automated segmentation of cardiomyocyte Z-disks from high-throughput scanning electron microscopy data

2019 ◽  
Vol 19 (S6) ◽  
Author(s):  
Afshin Khadangi ◽  
Eric Hanssen ◽  
Vijay Rajagopal
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
High Throughput ◽  
Scanning Electron Microscopy Data ◽  
Automated Segmentation ◽  
Advantages And Disadvantages ◽  
Face Scanning ◽  
Block Face ◽  
Microscopy Data ◽  
Scanning Electron

Abstract Background With the advent of new high-throughput electron microscopy techniques such as serial block-face scanning electron microscopy (SBF-SEM) and focused ion-beam scanning electron microscopy (FIB-SEM) biomedical scientists can study sub-cellular structural mechanisms of heart disease at high resolution and high volume. Among several key components that determine healthy contractile function in cardiomyocytes are Z-disks or Z-lines, which are located at the lateral borders of the sarcomere, the fundamental unit of striated muscle. Z-disks play the important role of anchoring contractile proteins within the cell that make the heartbeat. Changes to their organization can affect the force with which the cardiomyocyte contracts and may also affect signaling pathways that regulate cardiomyocyte health and function. Compared to other components in the cell, such as mitochondria, Z-disks appear as very thin linear structures in microscopy data with limited difference in contrast to the remaining components of the cell. Methods In this paper, we propose to generate a 3D model of Z-disks within single adult cardiac cells from an automated segmentation of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The proposed fully automated segmentation scheme is comprised of three main modules including “pre-processing”, “segmentation” and “refinement”. We represent a simple, yet effective model to perform segmentation and refinement steps. Contrast stretching, and Gaussian kernels are used to pre-process the dataset, and well-known “Sobel operators” are used in the segmentation module. Results We have validated our model by comparing segmentation results with ground-truth annotated Z-disks in terms of pixel-wise accuracy. The results show that our model correctly detects Z-disks with 90.56% accuracy. We also compare and contrast the accuracy of the proposed algorithm in segmenting a FIB-SEM dataset against the accuracy of segmentations from a machine learning program called Ilastik and discuss the advantages and disadvantages that these two approaches have. Conclusions Our validation results demonstrate the robustness and reliability of our algorithm and model both in terms of validation metrics and in terms of a comparison with a 3D visualisation of Z-disks obtained using immunofluorescence based confocal imaging.

Fractographic Regularities in Fatigue Failure of 17Mn1Si Steel

2014 ◽  
Vol 1040 ◽  
pp. 230-235
Author(s):  
Pavlo Maruschak ◽  
Sergey Panin ◽  
Ilya Vlasov ◽  
Iryna Danyliuk ◽  
Roman Bishchak
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Scanning Electron Microscopy Data ◽  
Structural Level ◽  
Deformation And Failure ◽  
Leading Role ◽  
Electron Microscopy Data ◽  
Microscopy Data ◽  
Scanning Electron

Using the scanning electron microscopy data the main regularities of the fatigue crack propagation in the 17Mn1Si steel were studied. Based on fracture surface observation and analysis one can testify that the transition of the leading role of deformation and failure from the lower structural level to the higher one has the ordered pattern.

scholarly journals A Guide to Analysis and Reconstruction of Serial Block Face Scanning Electron Microscopy Data

2017 ◽  
Author(s):  
E. Cocks ◽  
M. Taggart ◽  
F.C. Rind ◽  
K. White
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Image Analysis ◽  
Large Data ◽  
Data Sets ◽  
Scanning Electron Microscopy Data ◽  
Face Scanning ◽  
Contrast Structures ◽  
Block Face ◽  
Scanning Electron

AbstractSerial block face scanning electron microscopy (SBF-SEM) is a relatively new technique that allows the acquisition of serially sectioned, imaged and digitally aligned ultrastructural data. There is a wealth of information that can be obtained from the resulting image stacks but this presents a new challenge for researchers - how to computationally analyse and make best use of the large data sets produced. One approach is to reconstruct structures and features of interest in 3D. However the software programs can appear overwhelming, time consuming and not intuitive for those new to image analysis. There are a limited number of published articles that provide sufficient detail on how to do this type of reconstruction. Therefore the aim of this paper is to provide a detailed step-by-step protocol, videos and explanation on the types of analysis and programs that can be used. To showcase the programs, skeletal muscle from fetal and adult guinea pigs were used. The tissue was processed using the heavy metal protocol developed specifically for SBFSEM. Trimmed resin blocks were placed into a Zeiss Sigma SEM incorporating the Gatan 3View and the resulting image stacks were analysed in 3 different programs, Fiji, Amira and MIB, using a range of tools available for segmentation. The results from the image analysis comparison show that the analysis tools are often more suited to a type of structure. For example larger structures, such as nuclei and cells, can be segmented using interpolation, which speeds up analysis; single contrast structures, such as the nucleolus, can be segmented using the contrast-based thresholding tools. Knowing the nature of the tissue and its specific structures (complexity, contrast, if there are distinct membranes, size) will help to determine the best method for reconstruction and thus maximising output from valuable tissue.

scholarly journals An Automated Workflow for Segmenting Single Adult Cardiac Cells from Large-Volume Serial Block-Face Scanning Electron Microscopy Data

2018 ◽  
Author(s):  
Akter Hussain ◽  
Shouryadipta Ghosh ◽  
Siavash Beikoghli Kalkhoran ◽  
Derek J. Hausenloy ◽  
Eric Hanssen ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Heart Function ◽  
Cardiac Cells ◽  
Scanning Electron Microscopy Data ◽  
Cardiac Cell ◽  
Branching Points ◽  
Face Scanning ◽  
Block Face ◽  
Scanning Electron

ABSTRACTThis paper presents a new algorithm to automatically segment the myofibrils, mitochondria and nuclei within single adult cardiac cells that are part of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The algorithm only requires a set of manually drawn contours that roughly demarcate the cell boundary at routine slice intervals (every 50th, for example). The algorithm correctly classified pixels within the single cell with 97% accuracy when compared to manual segmentations. One entire cell and the partial volumes of two cells were segmented. Analysis of segmentations within these cells showed that myofibrils and mitochondria occupied 47.5% and 51.6% on average respectively, while the nuclei occupy 0.7% of the cell for which the entire volume was captured in the SBF-SEM dataset. Mitochondria clustering increased at the periphery of the nucleus region and branching points of the cardiac cell. The segmentations also showed high area fraction of mitochondria (up to 70% of the 2D image slice) in the sub-sarcolemmal region, whilst it was closer to 50% in the intermyofibrillar space. We finally demonstrate that our segmentations can be turned into 3D finite element meshes for cardiac cell computational physiology studies. We offer our large dataset and MATLAB implementation of the algorithm for research use at www.github.com/CellSMB/sbfsem-cardiac-cell-segmenter/. We anticipate that this timely tool will be of use to cardiac computational and experimental physiologists alike who study cardiac ultrastructure and its role in heart function.

scholarly journals Creating a virtual device for processing the results of sorption measurements in the study of zinc oxide nanorods

2020 ◽  
Vol 7 (4) ◽  
pp. 154-158
Author(s):  
E. V. Maraeva ◽  
N. V. Permiakov ◽  
Y. Y. Kedruk ◽  
L. V. Gritsenko ◽  
Kh. A. Abdullin
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Zinc Oxide ◽  
Zinc Oxide Nanorods ◽  
Scanning Electron Microscopy Data ◽  
Virtual Device ◽  
Electron Microscopy Data ◽  
Oxide Nanorods ◽  
Microscopy Data ◽  
Scanning Electron

The work is devoted to the creation of a virtual device (computer program) for processing the results of sorption analysis of nanomaterials, including for estimating the size of nanoparticles based on the specific surface area. The obtained evaluation results were compared with the scanning electron microscopy data. Photocatalytically active zinc oxide samples were chosen as the object of the study.

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