Contents

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).

(from the webpage) >This usage example shows how to produce two-color images from spectrally unmixed data sets. It was written for an Alexa647/Alexa700 measurement on the Würzburg 1 biplane setup as documented in [Aufmkolk2012]. The first two tasks in this example produce prerequisite knowledge for the image generation, the alignment information (Produce linear alignment matrix) and the F2 ratios, i.e. the relative intensity of fluorophores between the channels. [Aufmkolk2012] Hochauflösende Mehrfarben-Fluoeszenzmikroskopie. Sarah Aufmkolk. Julius-Maximilians-Universität Würzburg.

The ImageJ pligin, called PixFRET, allows a simple and rapid determination of channel bleed-through parameters and the display of normalized FRET images. see 2521

Input data type: 

Stacks with 2 channels (controls for bleed-through calculation) or 3 channels (FRET calculation)

Output data type: 

One Image with FRET values and One image with normalized FRET values

This macro is a plugin macro to the "Intelligent Imaging" workflow. It detects the Cytoo patterns (specific fluorsecence channel) and computes the occupancy (number of cells) of each pattern by analyzing the images of the DAPI channel. The analysis function can be easily extended to, for instance, only select the cells that are well spread on the patterns (by analyzing a third channel with a properly chosen marker of the cytoplasm).

as of 20180529, links are not working due to web defacement.

The Measure Rosette Area Tool allows to measure the area of the rosettes of arabidopsis plants.

See: http://dev.mri.cnrs.fr/projects/imagej-macros/wiki/Measure_Rosette_Area…

Example data: http://biii.eu/node/1146, http://biii.eu/node/1145

An example macro introduced in the documentation page of the ImageJ plugin Trainable Weka Segmentation (in Fiji, it's bundled). A segmentation protocol based on machine learning. Full macro is available in the "Download" Link. 

A clear tutorial for splitting connected particles (cells) in a binary mask.

[as of 20180524, the website is temporary not functioning do to web defacement - please check again later] This tutorial will exemplify basic rapidSTORM usage by showing how to convert an Andor SIF acquisition to a super-resoluted image with rapidSTORM.

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A deconvolution component applicable to confocal and STED microscopy. The MATLAB function fo this package implements the SGP method for n-dimensional object deblurring with the option of boundary effects removal. Although this is a preliminary version, results seem to be good from their paper (Zanella et al 2013).

CellDetector can detect cells (or other objects) in microscopy images such as histopathology, fluorescence, phase contrast, bright field, etc. It uses a machine learning-based method where a cell model is learned from simple dot annotations on a few images for training and predict on test sets. The installation requires some efforts but the instruction is well explained. Training parameters should be tuned for different datasets, but the default settings could be a good starting point.

This plugin will return on a full 256-grey level image (limitation in this version) or on a ROI several texture features such as described in Haralick publication. Can be run in batch mode.

This Javascript works in ImageJ to measure 3D intensity profile along cylindrical space with variable radius.

The website implements a set of computer vision algorithms designed to automatically process time-lapse images of fluorescently labeled focal adhesion proteins in motile cells.

The methods associated with the processing have been published in PLOS One and Cell. The publication describes a quantitative analysis of focal adhesion dynamics that have been imaged using TIRF. All image processing steps are well explained or referenced.

The workflow computes cell-based colocalisation of two stainings in 2-D images. Both pixel- and object-based readouts are provided and some pros and cons are discussed. Please read here for more information:

https://github.com/tischi/ImageAnalysisWorkflows/blob/master/CellProfil…

Input data type: 

images

Output data type: 

processed images, numbers, text file, csv files