An ImageJ plugin for DEFCoN, the fluorescence spot counter based on fully convolutional neural networks

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Estimate the frequency of hepatitis C virus infected cells based on the intensity of viral antigen associated immunofluorescence. 

The core is an ImageJ Macro, so it's easy to modify for one's own needs (Link to the code). 

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MorphoLibJ is a library of plugin for ImageJ with functionalities for image processing such as filtering, reconstructing, segmenting, etc... Tools are based on Mathematical morphology with more rigorous mathematical approach than in the standard tools of ImageJ in particular for surface (or perimeter) measurements which are usually based on voxel counting.

Programming language: JAVA

Among features:

* Morphological operations :  Dilation, Erosion, Opening,  Closing , Top hat (white and black), Morphological gradient (aka Beucher Gradient), Morphological Laplacian, Morphological reconstruction, Maxima/Minima , Extended Maxima/Minima -Watershed (classic or controlled) -Image overlay -Image labelling -Geodesic diameter -Region Adjacency Graph -Granulometry curves, morphological image analysis.

User skill : Life scientist, developers, analysts

several steps of morphological segmentation of plant tissue using MorphoLibJ.

Colony Blob Count Tool


Count bacterial colonies on agar plates and measure the occupied surfaces. The user has to provide a selection (roi) of the area that will be analyzed. He can than run the segmentation and if necessary correct the results. In a third step he can run the counting and measurement.

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An easy to use, image analysis software package that enables rapid exploration and interpretation of microscopy data.


Counting Clumped Cells


A workflow combining ImageJ macro and manually using Trainable Weka Segmentation plugin for counting clumped cells.



ilastik is a simple, user-friendly tool for interactive image classification, segmentation and analysis. It is built as a modular software framework, which currently has workflows for automated (supervised) pixel- and object-level classification, automated and semi-automated object tracking, semi-automated segmentation and object counting without detection. Most analysis operations are performed lazily, which enables targeted interactive processing of data subvolumes, followed by complete volume analysis in offline batch mode. Using it requires no experience in image processing.

ilastik (the image learning, analysis, and segmentation toolkit) provides non-experts with a menu of pre-built image analysis workflows. ilastik handles data of up to five dimensions (time, 3D space, and spectral dimension). Its workflows provide an interactive experience to give the user immediate feedback on the quality of the results yielded by her chosen parameters and/or labelings.

The most commonly used workflow is pixel classification, which requires very little parameter tuning and instead offers a machine learning technique for segmenting an image based on local image features computed for each pixel.

Other workflows include:

Object classification: Similar to pixel classification, but classifies previously segmented objects by object characteristics in a subsequent step

Autocontext: This workflow improves the pixel classification workflow by running it in multiple stages and showing each pixel the results of the previous stage.

Carving: Semi-automated segmentation of 3D objects (e.g. neurons) based on user-provided seeds

Manual Tracking: Semi-automated cell tracking of 2D+time or 3D+time images based on manual annotations

Automated tracking: Fully-automated cell tracking of 2D+time or 3D+time images with some parameter tuning

Density Counting: Learned cell population counting based on interactively provided user annotation

Strengths: interactive, simple interface (for non-experts), few parameters, larger-than-RAM data, multi-dimensional data (time, 3D space, channel), headless operation, batch mode, parallelized computation, open source

Weaknesses: Pre-built workflows (not reconfigurable), no plugin system, visualization sometimes buggy, must import 3D data to HDF5, tracking requires an external CPLEX installation

Supported Formats: hdf5, tiff, jpeg, png, bmp, pnm, gif, hdr, exr, sif

3D Objects Counter

  • Counts the number of 3D objects in a stack.
  • quantifies for each found object the following parameters:
    • 3D intensity related measurement (with possible redirection to an image with the actual intensity value to be measured, for example for two channels measurements)
    • Volume and shape factors measurements, surface etc...
  • generates results representations such as:
    • Objects' map;
    • Surface voxels' map;
    • Centroids' map;
    • Centres of masses' map.

As ImageJ's “Analyze Particles” function, 3D-OC also has a “redirect to” option, allowing one image to be taken as a mask to quantify intensity related parameters on a second image. But unlike the Analyze particle, it include a thresholding option, meaning that you can start from a gray level  stack, not necessarily a binary mask.

To use it, first set the list of measurements by editing 3D OC Options. Both (3D Object counter and 3D OC Options are now in the default Fiji "Analyze" menu.

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Cell Counter ImageJ


An ImageJ plugin (author: Kurt De Vos) for counting multiple cell classes manually, which overlays already-counted cells on the image. It is controlled via its own graphical user interface, and can export and load results.

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