Object counting

ModularImageAnalysis (MIA) is an ImageJ plugin which provides a modular framework for assembling image and object analysis workflows. Detected objects can be transformed, filtered, measured and related. Analysis workflows are batch-enabled by default, allowing easy processing of high-content datasets.

MIA is designed for “out-of-the-box” compatibility with spatially-calibrated 5D images, yielding measurements in both pixel and physical units.  Functionality can be extended both internally, via integration with SciJava’s scripting interface, and externally, with Java modules that extend the MIA framework. Both have full access to all objects and images in the analysis workspace.

Workflows are, by default, compatible with batch processing multiple files within a single folder. Thanks to Bio-Formats, MIA has native support for multi-series image formats such as Leica .lif and Nikon .nd2.

Workflows can be automated from initial image loading through processing, object detection, measurement extraction, visualisation, and data exporting. MIA includes near 200 modules integrated with key ImageJ plugins such as Bio-Formats, TrackMate and Weka Trainable Segmentation.

Module(s) can be turned on/off dynamically in response to factors such as availability of images and objects, user inputs and measurement-based filters. Switches can also be added to “processing view” for easy workflow control.

MIA is developed in the Wolfson Bioimaging Facility at the University of Bristol.

The tool allows to measure the area of the invading spheroïd in a 3D cell invasion assay. It can also count and measure the area of the nuclei within the spheroïd.

QuantiFish is a quantification program intended for measuring fluorescence in images of zebrafish, although use with images of other specimens is possible. This package is geared towards analysis of fluorescent infection models. The software is designed to automate processing of images of single fish, and outputs results as a .csv file. Alongside measures of total fluorescence above a threshold, this package also introduces several measures for dissemination and distribution of fluorescence throughout the specimen.

CLIJ2 is a GPU-accelerated image processing library for ImageJ/Fiji, Icy, Matlab and Java. It comes with hundreds of operations for filtering, binarizing, labeling, measuring in images, projections, transformations and mathematical operations for images. While most of these are classical image processing operations, CLIJ2 also allows performing operations on matrices potentially representing neighborhood relationships between cells and pixels.

CLIJ2 was developed to process images from fluorescence microscopy data of developing cells, tissues, organoids and organisms.

3-D density kernel estimation (DKE-3-D) method, utilises an ensemble of random decision trees for counting objects in 3D images. DKE-3-D avoids the problem of discrete object identification and segmentation, common to many existing 3-D counting techniques, and outperforms other methods when quantification of densely packed and heterogeneous objects is desired. 

This notebook uses the rOMERO-gateway and EBImage to process an Image associated to the paper 'Timing of gene expression in a cell-fate decision system'.

The Image "Pos22" is taken from the dataset idr0040-aymoz-singlecell/experimentA/YDA306_AGA1y_PRM1r_Mating. It is a timelapse Image with 42 timepoints separated by 5 minutes. This Image is used to fit a model for the growth of the yeast cells. The notebook does not replicate any of the analysis of the above mentioned paper.

Its purpose is mainly to demonstrate the use of Jupyter, rOMERO-gateway and EBimage.

What it does:

• For each time point of one movie:
  • Read the image for this time point  from the IDR
  • Threshold the images and count the cells using EBimage functions
• Fit an exponential model to the count of cells against time to get a coefficient of grow (exponential factor)

This one example workflow from the Cell Profiler(CP)  Examples . CP is commonly used to count cells or other objects as well as percent-positives, by measuring the per-cell staining intensity. This pipeline shows how to do both of these tasks, and demonstrates how various modules may be used to accomplish the same result. 

In a few words, it used the IdentifyPrimaryObject module of CellProfiler to detect nuclei from a channel (e.g DAPI), then again the same module on another channel to detect another probe (e.g some particular histone)  .

Then objects (nuclei) are related to the second object (Histone), to create a parent child-relation ship: where nuclei can have histone has child. Nuclei are then filtered according to the property of having histone (positive) or not having histone (negtiveobject) related to them.  If needed, nuclei can be expanded in order to include touching object rather than object inside only.

The percentage of positive nuclei vs total number of nuclei can then be computed using the CalculateMath Module.

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

A workflow in Python to measure muscule fibers corresponding to the method used in Keefe, A.C. et al. Muscle stem cells contribute to myofibres in sedentary adult mice. Nat. Commun. 6:7087 doi: 10.1038/ncomms8087 (2015).

Example image:

muscleQNT/15536_2032_0.tif ...

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). 

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.  

http://imagej.net/MorphoLibJ#Measurements

Among the 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.

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.

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

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

This workflow estimates (densely distributed) object counts by the density of objects in the image without performing segmentation or object detection. Current version only works for 2D images of roundish objects with similar sizes on relatively homogeneous background. Users should provide a few labels of background and objects (especially on clustered objects), and the tool predicts the density of objects on the entire image. Counting is then estimated by integrating the density values on the whole image or specified rectangular regions of interests.

• 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.

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.

This tool compute measures on the ROIs of the chosen sequence, updates the measures live when ROIs are changed and allows to copy/paste the measures to 3rd-party sheet edition softwares. Measures include geometric (bounding box) and intensity information.

It can complement the default ICY built inROI table, where measurements such as volume meausirements, intensity measurements, ... are built in and can be exported as excel as well.