Globals for Images · SimFCS

Description

Software for analysis, visualization, simulation, and acquisition  of data from spectroscopy and fluorescence microscopy.

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NeuriteTracer

Description

"The plugin analyzes fluorescence microscopy images of neurites and nuclei of dissociated cultured neurons. Given user-defined thresholds, the plugin counts neuronal nuclei, and traces and measures neurite length."[...]" NeuriteTracer is a fast simple-to-use ImageJ plugin for the analysis of outgrowth in two-dimensional fluorescence microscopy images of neuronal cultures. The plugin performed well on images from three different types of neurons with distinct morphologies."

This plugin requires parameter setting: Threshold levels and scale (see more details on the related publication)

ORION

Description

ORION: Online Reconstruction and functional Imaging Of Neurons: segmentation and tracing of neurons for reconstruction.

A project to develop tools that explore single neuron function via sophisticated image analysis. ORION software bridges advanced optical imaging and compartmental modeling of neuronal function by rapidly, accurately, and robustly generating, from structural image data, a cylindrical morphology model suitable for simulating neuronal function. The goal of this project is to develop a computational and experimental framework to allow real-time mapping of functional imaging data (e.g., spatio-temporal patterns of dendritic voltages or intracellularions) to neuronal structure, during the very limited duration of an acute experiment.

ORION_example_result

AVEMAP

Description

Measures wound-healing assay videos, 

 For each video, the velocity and the order parameter are analyzed in time and space to extract quantitative parameters characterizing the cell motility phenotype. The different conditions (videos) can then be classified according to these parameters.

AveMAP

QuimP

Description

Summary

QuimP is software for tracking cellular shape changes and dynamic distributions of fluorescent reporters at the cell membrane. QuimP's unique selling point is the possibility to aggregate data from many cells in form of spatio-temporal maps of dynamic events, independently of cell size and shape. QuimP has been successfully applied to address a wide range of problems related to cell movement in many different cell types. 

Introduction

In transmembrane signalling the cell membrane plays a fundamental role in localising intracellular signalling components to specific sites of action, for example to reorganise the actomyosin cortex during cell polarisation and locomotion. The localisation of different components can be directly or indirectly visualised using fluorescence microscopy, for high-throughput screening commonly in 2D. A quantitative understanding demands segmentation and tracking of whole cells and fluorescence signals associated with the moving cell boundary, for example those associated with actin polymerisation at the cell front of locomoting cells. As regards segmentation, a wide range of methods can be used (threshold based, region growing, active contours or level sets) to obtain closed cell contours, which then are used to sample fluorescence adjacent to the cell edge in a straightforward manner. The most critical step however is cell edge tracking, which links points on contours at time t to corresponding points at t+1. Optical flow methods have been employed, but usually fail to meet the requirement that total fluorescence must not change. QuimP uses a method (ECMM, electrostatic contour migration method (Tyson et al., 2010) which has been shown to outperform traditional level set methods. ECMM minimises the sum of path lengths connecting all pairs of points, equivalent to minimising the energy required for cell deformation. The original segmentation based on an active contour method and outline tracking algorithms have been described in (Dormann et al., 2002; Tyson et al., 2010; Tyson et al., 2014).

Screenshot

Angiogenesis / Sprout Analyzer

Description

The Sprout Morphology plugin measures sprout number, length, width and cell density of endothelial cell (EC) sprouts grown in a bead sprouting assay. It optionally includes measuring the coverage of these sprouts with pericytes included in the assay, as well as the endothelial cell/pericyte ratio.

graphical abstract

SOAX

Description

SOAX is an open source software tool to extract the centerlines, junctions and filament lengths of biopolymer networks in 2D and 3D images. It facilitates quantitative, reproducible and objective analysis of the image data. The underlying method of SOAX uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then stretch along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments.

SOAX provides 3D visualization for exploring image data and visually checking results against the image. Quantitative analysis functions based on extracted networks are also implemented in SOAX, including spatial distribution, orientation, and curvature of filamentous structures. SOAX also provides interactive manual editing to further improve the extraction results, which can be saved in a file for archiving or further analysis. Useful for microtubules or actin filaments.

Observation: Depending on the operating system, the installation may or may not require Boost C++, ITK and VTK libraries. Windows has a standalone executable application without the need of those. 

snapshot microtubules soax

JACoP

Description

This ImageJ plug-in is a compilation of co-localization tools. It allows:

-Calculating a set of commonly used co-localization indicators:

Pearson's coefficient Overlap coefficient k1 & k2 coefficients Manders' coefficient Generating commonly used visualizations:

-Cytofluorogram

Having access to more recently published methods:

-Costes' automatic threshold

Li's ICA Costes' randomization Objects based methods (2 methods: distances between centres and centre-particle coincidence)

example of partial colocalisation from reference publication

SLOTH

Description

A collection for tracking microtubule dynamics, written in Python.

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Zebrafish Embryo Tool

Description

Normalize the orientation of the images of the Zebrafish embryos.

In the documentation webpage, the aim of the workflow is to normalize the orientation of the images of the Zebrafish embryos, find the point of injection of tumor cells and measure the distribution of Cy3 stained tumor foci.

ImageJ macro implementation of the Workflow described in Ghotra et al (2012). Note that currently only the angle and orientation normalization is implemented in this version.

Sample images are linked in the documentation webpage. 

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