scholarly journals Using TNT-NN to unlock the fast full spatial inversion of large magnetic microscopy data sets

2019 ◽  
Vol 71 (1) ◽  
Author(s):  
Joseph M. Myre ◽  
Ioan Lascu ◽  
Eduardo A. Lima ◽  
Joshua M. Feinberg ◽  
Martin O. Saar ◽  
...  
Keyword(s):  
Data Sets ◽  
Magnetic Microscopy ◽  
Microscopy Data

scholarly journals Automated tracking of S. pombe spindle elongation dynamics

2020 ◽  
Author(s):  
Ana Sofía M. Uzsoy ◽  
Parsa Zareiesfandabadi ◽  
Jamie Jennings ◽  
Alexander F. Kemper ◽  
Mary Williard Elting
Keyword(s):  
Principal Axis ◽  
Model Organism ◽  
Data Sets ◽  
Mitotic Progression ◽  
Automated Tracking ◽  
Spindle Elongation ◽  
Microscopy Data ◽  
Spindle Structure ◽  
Over Time ◽  
Imagej Plugin

The mitotic spindle is a microtubule-based machine that pulls the two identical sets of chromosomes to opposite ends of the cell during cell division. The fission yeast Schizosaccharomyces pombe is an important model organism for studying mitosis due to its simple, stereotyped spindle structure and well-established genetic toolset. S. pombe spindle length is a useful metric for mitotic progression, but manually tracking spindle ends in each frame to measure spindle length over time is laborious and can limit experimental throughput. We have developed an ImageJ plugin that can automatically track S. pombe spindle length over time and replace manual or semi-automated tracking of spindle elongation dynamics. Using an algorithm that detects the principal axis of the spindle and then finds its ends, we reliably track the length and angle of the spindle as the cell divides. The plugin integrates with existing ImageJ features, exports its data for further analysis outside of ImageJ, and does not require any programming by the user. Thus, the plugin provides an accessible tool for quantification of S. pombe spindle length that will allow automatic analysis of large microscopy data sets and facilitate screening for effects of cell biological perturbations on mitotic progression.

The Use of Data Explorer as a 3-D Reconstruction Tool for Microscopy Data Sets

1997 ◽  
Vol 3 (S2) ◽  
pp. 1131-1132
Author(s):  
Jansma P.L ◽  
M.A. Landis ◽  
L.C. Hansen ◽  
N.C. Merchant ◽  
N.J. Vickers ◽  
...  
Keyword(s):  
Volume Rendering ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Building Blocks ◽  
General Purpose ◽  
Data Sets ◽  
Laser Scanning Confocal Microscope ◽  
Use Of Data ◽  
Using Data ◽  
Microscopy Data

We are using Data Explorer (DX), a general-purpose, interactive visualization program developed by IBM, to perform three-dimensional reconstructions of neural structures from microscopic or optical sections. We use the program on a Silicon Graphics workstation; it also can run on Sun, IBM RS/6000, and Hewlett Packard workstations. DX comprises modular building blocks that the user assembles into data-flow networks for specific uses. Many modules come with the program, but others, written by users (including ourselves), are continually being added and are available at the DX ftp site, http://www.tc.cornell.edu/DXhttp://www.nice.org.uk/page.aspx?o=43210.Initally, our efforts were aimed at developing methods for isosurface- and volume-rendering of structures visible in three-dimensional stacks of optical sections of insect brains gathered on our Bio-Rad MRC-600 laser scanning confocal microscope. We also wanted to be able to merge two 3-D data sets (collected on two different photomultiplier channels) and to display them at various angles of view.

scholarly journals Large-scale analysis of high-speed atomic force microscopy data sets using adaptive image processing

2012 ◽  
Vol 3 ◽  
pp. 747-758 ◽  
Author(s):  
Blake W Erickson ◽  
Séverine Coquoz ◽  
Jonathan D Adams ◽  
Daniel J Burns ◽  
Georg E Fantner
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Data Sets ◽  
Force Microscopy ◽  
Topographic Features ◽  
Atomic Force ◽  
Large Scale Analysis ◽  
Adaptive Image Processing ◽  
True Representation ◽  
Microscopy Data

Modern high-speed atomic force microscopes generate significant quantities of data in a short amount of time. Each image in the sequence has to be processed quickly and accurately in order to obtain a true representation of the sample and its changes over time. This paper presents an automated, adaptive algorithm for the required processing of AFM images. The algorithm adaptively corrects for both common one-dimensional distortions as well as the most common two-dimensional distortions. This method uses an iterative thresholded processing algorithm for rapid and accurate separation of background and surface topography. This separation prevents artificial bias from topographic features and ensures the best possible coherence between the different images in a sequence. This method is equally applicable to all channels of AFM data, and can process images in seconds.

scholarly journals 3D fluorescence microscopy data synthesis for segmentation and benchmarking

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260509
Author(s):  
Dennis Eschweiler ◽  
Malte Rethwisch ◽  
Mareike Jarchow ◽  
Simon Koppers ◽  
Johannes Stegmaier
Keyword(s):  
Fluorescence Microscopy ◽  
Image Data ◽  
Training Data ◽  
Cellular Structures ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Automated Generation ◽  
3D Fluorescence Microscopy ◽  
Manual Interaction ◽  
Microscopy Data

Automated image processing approaches are indispensable for many biomedical experiments and help to cope with the increasing amount of microscopy image data in a fast and reproducible way. Especially state-of-the-art deep learning-based approaches most often require large amounts of annotated training data to produce accurate and generalist outputs, but they are often compromised by the general lack of those annotated data sets. In this work, we propose how conditional generative adversarial networks can be utilized to generate realistic image data for 3D fluorescence microscopy from annotation masks of 3D cellular structures. In combination with mask simulation approaches, we demonstrate the generation of fully-annotated 3D microscopy data sets that we make publicly available for training or benchmarking. An additional positional conditioning of the cellular structures enables the reconstruction of position-dependent intensity characteristics and allows to generate image data of different quality levels. A patch-wise working principle and a subsequent full-size reassemble strategy is used to generate image data of arbitrary size and different organisms. We present this as a proof-of-concept for the automated generation of fully-annotated training data sets requiring only a minimum of manual interaction to alleviate the need of manual annotations.

scholarly journals Segmentation of Static and Dynamic Atomic-Resolution Microscopy Data Sets with Unsupervised Machine Learning Using Local Symmetry Descriptors

2021 ◽  
pp. 1-11
Author(s):  
Ning Wang ◽  
Christoph Freysoldt ◽  
Siyuan Zhang ◽  
Christian H. Liebscher ◽  
Jörg Neugebauer
Keyword(s):  
Machine Learning ◽  
Feature Space ◽  
Local Symmetry ◽  
Atomic Resolution ◽  
Video Sequences ◽  
Data Sets ◽  
Unsupervised Machine Learning ◽  
Transmission Electron ◽  
Scanning Transmission ◽  
Microscopy Data

We present an unsupervised machine learning approach for segmentation of static and dynamic atomic-resolution microscopy data sets in the form of images and video sequences. In our approach, we first extract local features via symmetry operations. Subsequent dimension reduction and clustering analysis are performed in feature space to assign pattern labels to each pixel. Furthermore, we propose the stride and upsampling scheme as well as separability analysis to speed up the segmentation process of image sequences. We apply our approach to static atomic-resolution scanning transmission electron microscopy images and video sequences. Our code is released as a python module that can be used as a standalone program or as a plugin to other microscopy packages.

scholarly journals ShareLoc – an open platform for sharing localization microscopy data

2021 ◽  
Author(s):  
Jiachuan Bai ◽  
Wei Ouyang ◽  
Manish Kumar Singh ◽  
Christophe Leterrier ◽  
Paul Barthelemy ◽  
...  
Keyword(s):  
Single Molecule ◽  
Super Resolution ◽  
Data Sets ◽  
Localization Microscopy ◽  
Open Platform ◽  
Machine Learning Methods ◽  
Microscopy Data ◽  
Analytical Tools ◽  
Existing Data ◽  
Single Molecule Localization Microscopy

Novel insights and more powerful analytical tools can emerge from the reanalysis of existing data sets, especially via machine learning methods. Despite the widespread use of single molecule localization microscopy (SMLM) for super-resolution bioimaging, the underlying data are often not publicly accessible. We developed ShareLoc (https://shareloc.xyz), an open platform designed to enable sharing, easy visualization and reanalysis of SMLM data. We discuss its features and show how data sharing can improve the performance and robustness of SMLM image reconstruction by deep learning.

Hardware Accelerated Segmentation of Complex Volumetric Filament Networks

2009 ◽  
Vol 15 (4) ◽  
pp. 670-681 ◽  
Author(s):  
David Mayerich ◽  
John Keyser
Keyword(s):  
High Throughput ◽  
Large Data ◽  
Graphics Hardware ◽  
Data Sets ◽  
Volume Data ◽  
Data Set ◽  
Thin Filaments ◽  
Novel Method ◽  
Filament Networks ◽  
Microscopy Data

We present a framework for segmenting and storing filament networks from scalar volume data. Filament networks are encountered more and more commonly in biomedical imaging due to advances in high-throughput microscopy. These data sets are characterized by a complex volumetric network of thin filaments embedded in a scalar volume field. High-throughput microscopy volumes are also difficult to manage since they can require several terabytes of storage, even though the total volume of the embedded structure is much smaller. Filaments in microscopy data sets are difficult to segment because their diameter is often near the sampling resolution of the microscope, yet these networks can span large regions of the data set. We describe a novel method to trace filaments through scalar volume data sets that is robust to both noisy and undersampled data. We use graphics hardware to accelerate the tracing algorithm, making it more useful for large data sets. After the initial network is traced, we use an efficient encoding scheme to store volumetric data pertaining to the network.

scholarly journals Learning cellular morphology with neural networks

2018 ◽  
Author(s):  
Philipp J Schubert ◽  
Sven Dorkenwald ◽  
Michał Januszewski ◽  
Viren Jain ◽  
Joergen Kornfeld
Keyword(s):  
Neural Networks ◽  
Cell Types ◽  
Cellular Morphology ◽  
Data Sets ◽  
Neuron Reconstruction ◽  
Diverse Application ◽  
Unsupervised Training ◽  
Microscopy Data ◽  
Volume Electron Microscopy ◽  
Made In

AbstractReconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging, but can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated neuron reconstruction, as well as automated detection of synapses. However, methods for automating the morphological analysis of nanometer-resolution reconstructions are less established, despite their diverse application possibilities. Here, we introduce cellular morphology neural networks (CMNs), based on multi-view projections sampled from automatically reconstructed cellular fragments of arbitrary size and shape. Using unsupervised training we inferred morphology embeddings (“Neuron2vec”) of neuron reconstructions and trained CMNs to identify glia cells in a supervised classification paradigm which was then used to resolve neuron reconstruction errors. Finally, we demonstrate that CMNs can be used to identify subcellular compartments and the cell types of neuron reconstructions.

scholarly journals A simple and flexible computational framework for inferring sources of heterogeneity from single-cell dynamics

2018 ◽  
Author(s):  
Lekshmi Dharmarajan ◽  
Hans-Michael Kaltenbach ◽  
Fabian Rudolf ◽  
Joerg Stelling
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Mechanistic Model ◽  
Time Lapse ◽  
Data Sets ◽  
Multiple Sources ◽  
Nonlinear Mixed Effects Models ◽  
Cell Dynamics ◽  
Important Open Problem ◽  
Microscopy Data

AbstractThe availability of high-resolution single-cell data makes data analysis and interpretation an important open problem, for example, to disentangle sources of cell-to-cell and intra-cellular variability. Nonlinear mixed effects models (NLMEs), well established in pharmacometrics, account for such multiple sources of variations, but their estimation is often difficult. Single-cell analysis is an even more challenging application with larger data sets and models that are more complicated. Here, we show how to leverage the quality of time-lapse microscopy data with a simple two-stage method to estimate realistic dynamic NLMEs accurately. We demonstrate accuracy by benchmarking with a published model and dataset, and scalability with a new mechanistic model and corresponding dataset for amino acid transporter endocytosis in budding yeast. We also propose variation-based sensitivity analysis to identify time-dependent causes of cell-to-cell variability, highlighting important sub-processes in endocytosis. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

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