Cell or particle counting and scoring the percentage of stained objects

Description

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.

Positivepercentcell

Cell or particle Counting and scoring stained objects using CellProfiler

Description

This is a Jupyter notebook demonstrating the run of a code from IDR data sets by loading a CellProfiler Pipeline 

The example here is applied on real data set, but does not correspond to a biological question. It aims to demonstrate how to create a jupyter notebook to process online plates hosted in the IDR.

It reads the plate images from the IDR.

It loads the CellProfiler Pipeline and replace the reading modules used to read local files from this defaults pipeline by module allowing to read data remotely accessible.

It creates a CSV file and displays it in the notebook.

It makes some plot with Matplotlib.

 

jupyter

Orbit

Description

Orbit Image Analysis is a free open source software with the focus to quantify big images like whole slide scans.

It can connect to image servers, e.g. Omero.
Analysis can be done on your local computer or via scaleout functionality in a distrubuted computing environment like a Spark cluster.

Sophisticated image analysis algorithms incl. tissue quantification using machine learning, object segmentation and classification are build in. In addition a versatile API allows you to enhance Orbit and to run your own scripts.

Orbit

MAARS

Description

automated open-source image acquisition and on-the-fly analysis pipeline (initially developped for analysis of mitotic defects in fission yeast)

maars workflow from publication

 

maars

McLuigi

Description

Multicut workflow for large connectomics data. Using luigi for pipelining and caching processing steps. Most of the computations are done out-of-core using hdf5 as backend and implementations from nifty

classification of hemp fibers based on morphological features

Description

 

In this workflow, you can use MorphoLibJ to generate accurate morphometric measurements

  • First the fibers are segmented by mathematical morphology:
    • for example by using MorphoLibJ:
      • Create a marker image by creating a rough mask with extended regional maxima (similar to Find Max), such that you have one max per fiber
      • Use the marker controlled watershed (in MorpholLibJ/ Segmentation/ marker controlled watershed) : indicate the original grayscale image as the input, Marker will be your maxima image, select None for mask
      • it will create a label mask of your fibers
  •  In MorphoLibJ /analyze/ select Region Morphometry: this will compute different shape factors which are more robust than the ones implemented by default in ImageJ
  • Export the result table created to a csv file
  • Then for example in Matlab or R, you can apply a PCA analysis (Principal component analysis) followed by a k-means with the number of class (clusters) (different fibers type) you want to separate.
  • You can then add this class as a new feature to your csv file.
  • From this you can sort your labelled fibers into these clusters for a visual feedback or further spatial analysis
has topic
hemp analysis

ImmunoMembrane

Description

Quantification of HER2 immunohistochemistry.

ImmunoMembrane is an ImageJ plugin for assessing HER2 immunohistochemistry, described in [bib]2472[/bib]. It is important to read the URL documentation and original paper to understand how to use the plugin appropriately.

There is web service available. Users can upload image data to process them and get cell membrane to be segmented: Web ImmunoMembrane

Note also that 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).

has topic

Object Classification using ilastik

Description

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.

ilastik

Description

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