Automated

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This is a simple example of a DNA damage assay using single cell gel electrophoresis. Here, the measurement of interest is the length and intensity of the comet tail. Also, illumination correction is used to reduce background fluorescence prior to measurement. Also shown is a silver-stained comet example in which the percentage of DNA contained in the tail is calculated.

Example Images: Packaged together with the cellprofiler pipeline file. 

Oufti (previously named MicrobeTracker) is a MATLAB application / suite of tools for analysing fluorescent spots inside microbes. MicrobeTracker can identify cell outlines and fluorescent foci, and generate plots and statistics based on positions and intensity (kymographs, histograms etc.) The MATLAB code is easy to modify and extend to add additional plots and statistics: see e.g. Lesterlin et al. (2014).

The Outfi Forum is quite active.

This workflow classifies objects based on object-level features (e.g. intensity based, morphology based, etc) and user annotations. It needs segmentation images besides the raw image data. Segmentation images can be obtained from ilastik pixel classification, or binary segmentation images from other tools. Within the object classification, one can prefilter objects through thresholds (on pixel probability image) or object sizes (on segmentation image). Outputs are predicted classification label images. Selected features can also be exported. Advanced users also have possibilities to add customized (object) features for classification in a simple plugin fashion through python scripts.

Simple spatial filters can be used to suppress noise in raw image data (i.e. by averaging intensities). The best choice of filter depends on the nature of the noise, but Gaussian filtering works well for Poisson noise (i.e. commonly observed photon-counting shot noise); whereas a median filter is ideal for salt-and-pepper noise. A larger filter radius leads to stronger noise suppression but more blurring. The URL above describes the simple 2D spatial filters available in ImageJ, but similar filters are available in most software. For 3D data, 3D versions of these filters work best (since there are more pixels to average within the same radius).

In this human cytoplasm-nucleus translocation assay, learn how to load a previously calculated illumination correction function for two separate channels, measure protein content in the nucleus and cytoplasm, and calculate the ratio as a measure of translocation. This is a clumpy cell type, so studying the settings in primary object identification may be helpful for users interested in the more advanced options that module offers. More about these images can be found at the BBBC.

This protocol first extracts the cell nuclei from a given fluorescence channel (full labeling), and grows a contour from each nucleus to extract the cell edge in another fluorescence channel (membrane-labeling).

The Macro processes a composite picture in ImageJ/Fiji and outputs a color-balanced merged RGB image.

To calculate the white balance, a rectangle at coordinates (x=100, y=100) and of size (w=100 pixels, h=100 pixels) is used. These values can be changed to make sure that a background region is taken for the calculation in the line: makeRectangle(100,100,100,100). The user could be prompted to draw the region by removing the signs // in the line: // waitForUser("Please draw a region in the background");

The original paper describes a method to analyze mitochondrial morphology in 2D and 3D.

The goal of this workflow is to track cells captured in a time-lapse movie of a syncytial blastoderm stage Drosophila embryo and quantify their movement.

This example shows an example of object tracking. This pipeline analyzes a time-lapse experiment to identify the cells and track them from frame to frame, which is challenging since the cells are also moving. In addition, this pipeline also extracts metadata from the filename and uses groups the images by metadata in order to independently process several sequences of images and output the measurements of each.

Sample images

A portion of a time lapse movie of a syncytial blastoderm stage Drosophila embryo with a GFP-histone gene which renders chromatin fluorescent in live embryos. The movie shows nuclear divisions 10 through 13.

Victoria Foe made this movie on a Bio-Rad Radiance 2000 laser scanning confocal microscope using a 40X 1.3NA oil objective. The frames are 7 seconds apart and plays at 30 frames per second

GFP-histone transformed files provided by Rob Saint

V.Foe and G.Odell, . 26 July 2001

Task

Quantify the length of microtubules (MT) and the MT average density per cell.

Workflow descriptions

Simple two step workflow, allowing visual & manual correction of microtubule between the 2 steps. Batch measurement of microtubule lengths for multiple images is achieved by segmenting the MTs and then their skeletonizations. The number of pixels in the microtubule is proportional to their length, so the length can be estimated.

Script

Workflow is written as an ImageJ macro (Fiji) with following steps:

1. The enhancement of tubular structure by computing eigenvalues of the hessian matrix on a Gaussian filtered version of the image ( sigma 1 pixel), as implemented in the tubeness plugin.

2. The tubules were then thresholded , and structures containing less than 3 pixels were discarded.

3. If needed, a visual check and correction of segmented microtubule is then performed.

4. After correction, segmented MTs were then reduced to a 1-pixel thick line using the skeletonize plugin of Fiji. The length of the skeletonized microtubules was then directly proportional to their length.

5. Data were grouped by condition and converted back to micrometers units under Matlab for the statistical tests.

Pitfalls

Commented but not that general without editing some fields in the macros.

Sample Data

Sample data and workflow (see above URL) can be accessed by - login: biii - password Biii!

Misc

3D version also available here. Use of components Skeletonize and Tubeness Filter

Analyzing ER, PR, and Ki-67 immunohistochemistry

ImmunoRatio is an ImageJ plugin to quantify haematoxylin and DAB-stained tissue sections by measuring the percentage of positively stained nuclear area (labeling index), described in [bib]2452[/bib].

Notes for use:

• It is important to read the URL instructions and original paper to understand what is being measured. In particular, the primary measurement made is percentage of the total nuclear area, not the percentage of detected nuclei (the latter being the more common method of assessing e.g. Ki67). This may be further modified by the Result correction equation.
• Ultimately ImmunoRatio relies on thresholding (color deconvolved [bib]2451[/bib]) images to define 'nucleus' vs 'non-nucleus' regions according to staining intensity. Therefore dark artefacts, such as tissue folds, are likely to cause errors.
• The pixel size is not read automatically from the image, but rather the source image scale should be entered into the dialog box - and the image rescaled accordingly prior to analysis. This scale value is the inverse of the value normally found for pixel width and pixel height under Image -> Properties... (i.e. pixel width & height are given in microns per pixel; the dialog box asks for pixels per micron).

Web application: ImmunoRatio

Example Image: Sample ImmunoRatio results

References

1. [2452] Tuominen VJ, Ruotoistenmäki S, Viitanen A, Jumppanen M, Isola J.  2010.  ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67.. Breast Cancer Res. 12(4):R56.
2. [2451] Ruifrok AC, Johnston DA.  2001.  Quantification of histochemical staining by color deconvolution.. Anal Quant Cytol Histol. 23(4):291-9.

This macro can be used to un-wide a tubular structure and flatten its surface (like peeling of and flattening the skin of a banana). The macro can only process a single channel 3D stack but it is easy to process multiple channels by exporting and importing ROI manager selections. Technically the macro computes the radial average intensity projection inside a ring centred on the radial symmetry axis of the object. The final image is a radial mapping of the intensity (radial angle along X, axial length along Y).

The example image is available in the documentation link. 

This macro implements a filter that is meant to attenuate close to parallel intensity stripes in an image, such as often happening in light sheet microscopy. The results are usually decent even when the stripes show a large angular spread due to light sheet refraction at the sample surface. The filter can process a 3D stack but the processing is performed slice by slice.

Example image is available in the documentation link. 

This macro batch processes all the 2D images (tif and jpg files) located in a user defined folder by calling Fiji Weka trainable segmentation to classify each pixel, and reports the areas of each class in a human readable results table. The classifier to be applied to each image should be previously trained on a representative image by an expert and exported to file (Save classifier) into the image folder to be processed.

This macro segments blood vessels in a 3D stack. It is suited for well-contrasted images (low background) and works better if the width of the vessels of interest is reasonably uniform.

Sample image: 1

sample image: 2

Autofocus hyperstack macro:

Select the in focus frame from each slice of a hyperstack and create a new stack of just the in focus frames. Based on algorithm F-11 "Normalized Variance".

Original macro by Andy Weller.

This macro performs measurements of average and standard deviation intensity inside wells of a protein microarray (the number of wells is limited to 250, the image should be cropped for larger arrays). The macro requires the "ImageJ plugins toolkit". To ensure compatibility with Fiji you should download the version 1.6.1. The installation instructions can be found here, it only consists in un-compressing the .jar file from the previous archive to Fiji plugins folder.

sample image: link

The macro segments and classifies human spermatozoids nuclei (DAPI) based on the number of FISH signals (spots) they contain. It reports the percentage of occurrences of user defined classes (combinations of spot multiplicity in the FISH channels) as well as the position (point selections) of the detected nuclei falling in these classes. The input image should be an hyperstack with 4 channels: DAPI (first channel) and three FISH channels. The images are typically obtained as a maximum intensity projection of few channels (confocal) or a single z slice acquisition (widefield).

Example image available in the linked page. 

A commercial image analysis software. It's interface allows to easily perform measurements and image analysis. Your actions can be recorded and a macro (in a basic script language) can then be created. Almost no knowledge in programming is needed. You can also use python. A SDK is also available to develop stand alone applications in c++. Additional modules allow to use specific operations (3D operators... Examples of available categories of operators : filtering, edge detection, mathematical morphology, segmentation, Frequency operations, mathematical/logical operations, measurements...

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This pipeline shows how to identify smaller objects (foci) within larger objects (nuclei) and how to use the Relate module to establish a relationship between the two as well as perform per-object aggregate measurements (such as number of foci per nucleus). Sample images are included in the download package.

The workflow includes segmentation, tracking and quantifying morphological dynamics of moving cells in 3D. The authors have implemented the workflow in Matlab, but so far there is no download link provided. To apply this workflow, we recommend to contact the authors or to implement the worflow based on the detailed description in the original paper.