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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. 

This Matlab code demonstrates an edge-based active contour model as an application of the Distance Regularized Level Set Evolution (DRLSE) formulation.

idTracker is a videotracking software that keeps the correct identity of each individual during the whole video. It works for many animal species including mice, insects (Drosophila, ants) and fish (zebrafish, medaka, stickleback). idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. Technique details and analyses of several applications are described in Pérez-Escudero et al (2014).

Using a text file containing 3D point coordinates as reference pairs, 3D image stack is transformed.

The workflow measures the growth of cells in 3D, combining an ImageJ macro for preprocessing and successive tracking using Imaris.  

Variational algorithms to remove stationary noise. Application to microscopy imaging. This plugin allows to denoise images degraded with stationary noise. Stationary noise can be seen as a generalization of the standard white noise. Typical applications of this plugin are:

- Standard white noise denoising using a total variation and fidelity term minimization. Even though total variation denoising is not the state of the art (regarding SNR improvement), it may be very valuable for further tasks such as image seg- mentation).

An automated MATLAB tool for segmentation of surface stained cells

DBSCAN (Density-based spatial clustering of applications with noise) performs multi-dimensional clustering based on the local density of points. This plugin is implemented for 2-3 dimensions.

Two workflows are proposed here:

one based on fiducials, the other one on cross-correlation.

A clear tutorial on how to write a MATLAB script to segment clustered cells.

The full script is downloadable near the bottom of the article. 

A workflow template to analyze subcellular structures in fluorescence 2D/3D microscopy images based on a Fiji plugin **Squassh** is described in Rizek et al (2014).

The workflow employs detecting, segmenting, and quantifying subcellular structures. For segmentation, it accounts for the microscope optics and for uneven image background. Further analyses include both colocalization and shape analyses. However, it does not work directly for time-lapse data. A brief summary note can be found here.

Matlab toolbox to analyze single molecule mRNA FISH data. Allows counting the number of mature and nascent transcripts in 3D images. See 2513. Following toolboxes are required: - Optimization toolbox - Statistics toolbox - Image processing toolbox - (Optional) Parallel processing toolbox

Input data type: 3D image

Output data type: CSV

The linked webpage presents a collection of ImageJ macros for Intelligent Imaging (Feedback to microscope system for the secondary scan). 

An ImageJ macro able to control some microscopes (Micro-manager or Leica CAM controlled) to acquire high resolution images of only some structures (e.g. isolated cells) or events (e.g. mitosis) within a sample. The scan is sequenced as a primary (low resolution monitoring) scan and a secondary (high resolution, multi-dimensional) scan.

A Jython script using the plugin : Register Virtual Stack Slices It takes a sequence of image slices stored in a folder, and delivers a list of registered image slices (with enlarged canvas). One of the images in the sequence can be selected by the user as reference and it will remain intact.

The rapidSTORM project is an open source evaluation tool that provides fast and highly configurable data processing for single-molecule localization microscopy such as dSTORM. It provides both two-dimensional and three-dimensional, multi-color data analysis as well as a wide range of filtering and image generation capabilities. The general operation of rapidSTORM is described in Wolter et al (2012).