scholarly journals High-Precision Automated Reconstruction of Neurons with Flood-filling Networks

2017 ◽  
Author(s):  
Michał Januszewski ◽  
Jörgen Kornfeld ◽  
Peter H. Li ◽  
Art Pope ◽  
Tim Blakely ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Path Length ◽  
Error Rates ◽  
Data Set ◽  
Neural Processes ◽  
Order Of Magnitude ◽  
Block Face ◽  
Microscopy Data ◽  
Better Than ◽  
Volume Electron Microscopy

AbstractReconstruction of neural circuits from volume electron microscopy data requires the tracing of complete cells including all their neurites. Automated approaches have been developed to perform the tracing, but without costly human proofreading their error rates are too high to obtain reliable circuit diagrams. We present a method for automated segmentation that, like the majority of previous efforts, employs convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of the reconstructed shape of individual neural processes. We used this technique, which we call flood-filling networks, to trace neurons in a data set obtained by serial block-face electron microscopy from a male zebra finch brain. Our method achieved a mean error-free neurite path length of 1.1 mm, an order of magnitude better than previously published approaches applied to the same dataset. Only 4 mergers were observed in a neurite test set of 97 mm path length.

A method of precise misorientation determination

2003 ◽  
Vol 36 (6) ◽  
pp. 1319-1323 ◽  
Author(s):  
A. Morawiec
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Spatial Resolution ◽  
Standard Procedure ◽  
Good Spatial Resolution ◽  
Transmission Electron ◽  
Order Of Magnitude ◽  
Achievable Accuracy ◽  
Better Than

A method that improves the accuracy of misorientations determined from Kikuchi patterns is described. It is based on the fact that some parameters of a misorientation calculated from two orientations are more accurate than other parameters. A procedure which eliminates inaccurate elements is devised. It requires at least two foil inclinations. The quality of the approach relies on the possibility to set large sample-to-detector distances and the availability of good spatial resolution of transmission electron microscopy. Achievable accuracy is one order of magnitude better than the accuracy of the standard procedure.

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.

scholarly journals Trends in the Electron Microscopy Data Bank (EMDB)

2017 ◽  
Vol 73 (6) ◽  
pp. 503-508 ◽  
Author(s):  
Ardan Patwardhan
Keyword(s):  
Electron Microscopy ◽  
Three Dimensional ◽  
Data Bank ◽  
Electron Microscopy Data ◽  
Technological Advances ◽  
Major Player ◽  
Leading Position ◽  
Microscopy Data ◽  
The Uk ◽  
Better Than

Recent technological advances, such as the introduction of the direct electron detector, have transformed the field of cryo-EM and the landscape of molecular and cellular structural biology. This study analyses these trends from the vantage point of the Electron Microscopy Data Bank (EMDB), the public archive for three-dimensional EM reconstructions. Over 1000 entries were released in 2016, representing almost a quarter of the total number of entries (4431). Structures at better than 6 Å resolution now represent one of the fastest-growing categories, while the share of annually released tomography-related structures is approaching 20%. The use of direct electron detectors is growing very rapidly: they were used for 70% of the structures released in 2016, in contrast to none before 2011. Microscopes from FEI have an overwhelming lead in terms of usage, and the use of theRELIONsoftware package continues to grow rapidly after having attained a leading position in the field. China is rapidly emerging as a major player in the field, supplementing the US, Germany and the UK as the big four. Similarly, Tsinghua University ranks only second to the MRC Laboratory for Molecular Biology in terms of involvement in publications associated with cryo-EM structures at better than 4 Å resolution. Overall, the numbers point to a rapid democratization of the field, with more countries and institutes becoming involved.

scholarly journals Systematic morphological and morphometric analysis of identified olfactory receptor neurons in Drosophila melanogaster

2021 ◽  
Author(s):  
Cesar Nava Gonzales ◽  
Quintyn McKaughan ◽  
Eric A Bushong ◽  
Kalyani Cauwenberghs ◽  
Renny Ng ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Sensory Neurons ◽  
Olfactory Receptor ◽  
Cell Biology ◽  
3D Models ◽  
Olfactory Receptor Neurons ◽  
Block Face ◽  
Receptor Neurons ◽  
Circuit Function ◽  
Volume Electron Microscopy

ABSTRACTThe biophysical properties of sensory neurons are influenced by their morphometric and morphological features, whose precise measurements require high-quality volume electron microscopy (EM). However, systematic surveys of these nanoscale characteristics for identified neurons are scarce. Here, we characterize the morphology of Drosophila olfactory receptor neurons (ORNs) across the majority of genetically identified sensory hairs. By analyzing serial block-face electron microscopy (SBEM) images of cryofixed antennal tissues, we compile an extensive morphometric dataset based on 122 reconstructed 3D models of 33 identified ORN types. In addition, we observe multiple novel features—including extracellular vacuoles within sensillum lumen, intricate dendritic branching, mitochondria enrichment in select ORNs, novel sensillum types, and empty sensilla containing no neurons—which raise new questions pertinent to cell biology and sensory neurobiology. Our systematic survey is critical for future investigations into how the size and shape of sensory neurons influence their responses, sensitivity and circuit function.

scholarly journals Systematic morphological and morphometric analysis of identified olfactory receptor neurons in Drosophila melanogaster

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Cesar Nava Gonzales ◽  
Quintyn McKaughan ◽  
Eric A Bushong ◽  
Kalyani Cauwenberghs ◽  
Renny Ng ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Sensory Neurons ◽  
Olfactory Receptor ◽  
Cell Biology ◽  
3D Models ◽  
Olfactory Receptor Neurons ◽  
Block Face ◽  
Receptor Neurons ◽  
Circuit Function ◽  
Volume Electron Microscopy

The biophysical properties of sensory neurons are influenced by their morphometric and morphological features, whose precise measurements require high-quality volume electron microscopy (EM). However, systematic surveys of nanoscale characteristics for identified neurons are scarce. Here, we characterize the morphology of Drosophila olfactory receptor neurons (ORNs) across the majority of genetically identified sensory hairs. By analyzing serial block-face electron microscopy (SBEM) images of cryofixed antennal tissues, we compile an extensive morphometric dataset based on 122 reconstructed 3D models for 33 of the 40 identified antennal ORN types. Additionally, we observe multiple novel features - including extracellular vacuoles within sensillum lumen, intricate dendritic branching, mitochondria enrichment in select ORNs, novel sensillum types, and empty sensilla containing no neurons - which raise new questions pertinent to cell biology and sensory neurobiology. Our systematic survey is critical for future investigations into how the size and shape of sensory neurons influence their responses, sensitivity and circuit function.

scholarly journals Segmentation-Enhanced CycleGAN

2019 ◽  
Author(s):  
Michał Januszewski ◽  
Viren Jain
Keyword(s):  
Electron Microscopy ◽  
Ground Truth ◽  
Perfect Reconstruction ◽  
Imaging Method ◽  
Training Procedure ◽  
Electron Microscopy Data ◽  
Segmentation Approach ◽  
Microscopy Data ◽  
Near Perfect Reconstruction ◽  
Volume Electron Microscopy

AbstractAlgorithmic reconstruction of neurons from volume electron microscopy data traditionally requires training machine learning models on dataset-specific ground truth annotations that are expensive and tedious to acquire. We enhanced the training procedure of an unsupervised image-to-image translation method with additional components derived from an automated neuron segmentation approach. We show that this method, Segmentation-Enhanced CycleGAN (SECGAN), enables near perfect reconstruction accuracy on a benchmark connectomics segmentation dataset despite operating in a “zero-shot” setting in which the segmentation model was trained using only volumetric labels from a different dataset and imaging method. By reducing or eliminating the need for novel ground truth annotations, SECGANs alleviate one of the main practical burdens involved in pursuing automated reconstruction of volume electron microscopy data.

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