scholarly journals A semi-automated approach to dense segmentation of 3D white matter electron microscopy

2020 ◽  
Author(s):  
Michiel Kleinnijenhuis ◽  
Errin Johnson ◽  
Jeroen Mollink ◽  
Saad Jbabdi ◽  
Karla L. Miller
Keyword(s):  
Electron Microscopy ◽  
White Matter ◽  
Ground Truth ◽  
Outer Diameter ◽  
Myelinated Axons ◽  
Unmyelinated Axons ◽  
White Matter Microstructure ◽  
3D Electron Microscopy ◽  
3D Em ◽  
G Ratio

ABSTRACTPurposeNeuroscience methods working on widely different scales can complement and inform each other. At the macroscopic scale, magnetic resonance imaging methods that estimate microstructural measures have much to gain from ground truth validation and models based on accurate measurement of that microstructure. We present an approach to generate rich and accurate geometric models of white matter microstructure through dense segmentation of 3D electron microscopy (EM).MethodsVolumetric data of the white matter of the genu of the corpus callosum of the adult mouse brain were acquired using serial blockface scanning electron microscopy (SBF-SEM). A segmentation pipeline was developed to separate the 3D EM data into compartments and individual cellular and subcellular constituents, making use of established tools as well as newly developed algorithms to achieve accurate segmentation of various compartments.ResultsThe volume was segmented into six compartments comprising myelinated axons (axon, myelin sheath, nodes of Ranvier), oligodendrocytes, blood vessels, mitochondria, and unmyelinated axons. The myelinated axons had an average inner diameter of 0.56 μm and an average outer diameter of 0.87 μm. The diameter of unmyelinated axons was 0.43 μm. A mean g-ratio of 0.61 was found for myelinated axons, but the g-ratio was highly variable between as well as within axons.ConclusionThe approach for segmentation of 3D EM data yielded a dense annotation of a range of white matter compartments that can be interrogated for their properties and used for in silico experiments of brain structure. We provide the resulting dense annotation as a resource to the neuroscience community.

scholarly journals DeepACSON: Automated Segmentation of White Matter in 3D Electron Microscopy

2019 ◽  
Author(s):  
Ali Abdollahzadeh ◽  
Ilya Belevich ◽  
Eija Jokitalo ◽  
Alejandra Sierra ◽  
Jussi Tohka
Keyword(s):  
Electron Microscopy ◽  
White Matter ◽  
Large Field ◽  
Sham Operation ◽  
Automated Segmentation ◽  
Low Resolution ◽  
Myelinated Axons ◽  
Shape Decomposition ◽  
3D Em ◽  
Instance Segmentation

ABSTRACTAutomated segmentation techniques are essential to tracing the entirety of ultrastructures in large three-dimensional electron microscopy (3D-EM) images of the brain tissue. Current automated techniques use deep convolutional neural networks (DCNNs) and rely on high-contrast cellular membranes to trace a small number of neuronal processes in very high-resolution EM datasets. We developed DeepACSON to segment large field-of-view, low-resolution 3D-EM datasets of white matter where tens of thousands of myelinated axons traverse the tissue. DeepACSON performs DCNN-based semantic segmentation and shape decomposition-based instance segmentation. With its top-down design, DeepACSON manages to account for severe membrane discontinuities inescapable with the low-resolution imaging. In particular, the instance segmentation of DeepACSON uses the tubularity of myelinated axons, decomposing an under-segmented myelinated axon into its constituent axons. We applied DeepACSON to ten serial block-face scanning electron microscopy datasets of rats after sham-operation or traumatic brain injury, segmenting hundreds of thousands of long-span myelinated axons, thousands of cell nuclei, and millions of mitochondria with excellent evaluation scores. DeepACSON quantified the morphology and spatial aspects of white matter ultrastructures, capturing nanoscopic morphological alterations five months after the injury.

scholarly journals Protocol for preparation of heterogeneous biological samples for 3D electron microscopy: a case study for insects

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexey A. Polilov ◽  
Anastasia A. Makarova ◽  
Song Pang ◽  
C. Shan Xu ◽  
Harald Hess
Keyword(s):  
Electron Microscopy ◽  
Biological Samples ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Three Dimensional ◽  
Entire Volume ◽  
Simple Protocol ◽  
3D Electron Microscopy ◽  
3D Em

AbstractModern morphological and structural studies are coming to a new level by incorporating the latest methods of three-dimensional electron microscopy (3D-EM). One of the key problems for the wide usage of these methods is posed by difficulties with sample preparation, since the methods work poorly with heterogeneous (consisting of tissues different in structure and in chemical composition) samples and require expensive equipment and usually much time. We have developed a simple protocol allows preparing heterogeneous biological samples suitable for 3D-EM in a laboratory that has a standard supply of equipment and reagents for electron microscopy. This protocol, combined with focused ion-beam scanning electron microscopy, makes it possible to study 3D ultrastructure of complex biological samples, e.g., whole insect heads, over their entire volume at the cellular and subcellular levels. The protocol provides new opportunities for many areas of study, including connectomics.

scholarly journals DeepACSON automated segmentation of white matter in 3D electron microscopy

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ali Abdollahzadeh ◽  
Ilya Belevich ◽  
Eija Jokitalo ◽  
Alejandra Sierra ◽  
Jussi Tohka
Keyword(s):  
Electron Microscopy ◽  
White Matter ◽  
Sham Operation ◽  
Automated Segmentation ◽  
Cell Nuclei ◽  
Deep Convolutional Neural Networks ◽  
Myelinated Axons ◽  
Morphological Alterations ◽  
Shape Decomposition ◽  
Instance Segmentation

AbstractTracing the entirety of ultrastructures in large three-dimensional electron microscopy (3D-EM) images of the brain tissue requires automated segmentation techniques. Current segmentation techniques use deep convolutional neural networks (DCNNs) and rely on high-contrast cellular membranes and high-resolution EM volumes. On the other hand, segmenting low-resolution, large EM volumes requires methods to account for severe membrane discontinuities inescapable. Therefore, we developed DeepACSON, which performs DCNN-based semantic segmentation and shape-decomposition-based instance segmentation. DeepACSON instance segmentation uses the tubularity of myelinated axons and decomposes under-segmented myelinated axons into their constituent axons. We applied DeepACSON to ten EM volumes of rats after sham-operation or traumatic brain injury, segmenting hundreds of thousands of long-span myelinated axons, thousands of cell nuclei, and millions of mitochondria with excellent evaluation scores. DeepACSON quantified the morphology and spatial aspects of white matter ultrastructures, capturing nanoscopic morphological alterations five months after the injury.

scholarly journals Isotropic 3D electron microscopy reference library of whole cells and tissues

2020 ◽  
Author(s):  
C. Shan Xu ◽  
Song Pang ◽  
Gleb Shtengel ◽  
Andreas Müller ◽  
Alex T. Ritter ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Cultured Cells ◽  
Ion Beam ◽  
Reference Library ◽  
Whole Cells ◽  
3D Electron Microscopy ◽  
Mouse Pancreatic Islets ◽  
Focus Ion Beam ◽  
3D Em ◽  
Em Tomography

SummaryUnderstanding 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 imaging whole cells and tissues with enhanced Focus Ion Beam Scanning Electron Microscopy (FIB-SEM) in high resolution with month-long acquisition duration. We use this approach to generate reference 3D image datasets at 4-nm isotropic voxels for ten different examples, including cultured cells (cancer, macrophages, and T-cells) as well as tissues (mouse pancreatic islets and the Drosophila fan-shaped body). We open access to all datasets in OpenOrganelle, an interactive web platform that allows accessing both the original 3D EM data, and subsequent organelle segmentation. Together, these data will serve as a reference library to explore comprehensive quantification of whole cells and their constituents, thus addressing questions related to cell identities, cell morphologies, cell-cell interactions, as well as intracellular organelle organization and structure.

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