Digital histology

[no download link, this description itself explains the steps to quantify staining in tissue sections] The Color Deconvolution plugin for ImageJ can be used to digitally separate up to three stains from brightfield images, after which standard ImageJ commands can be used. The algorithm is described in Ruifork and Johnston (2001). **However**, it is **very** important to take into consideration the caveats on the linked URL. In particular, note that: - Stain colors depend on numerous factors, such as the precise stains and scanner; therefore, the 'default' stain vectors (used to define the colors) are unlikely to be optimal and may be very inaccurate. See the URL instructions for how to create new stain vectors. - Pixel values should be interpreted with extreme caution; in particular, note the warning regarding 'brown' staining that *attempting to quantify DAB intensity using this plugin is not a good idea*. Note, the pixel values provided by this plugin are 8-bit and **not** equivalent to 'optical densities' frequently presented in the literature. Color deconvolution is particularly helpful in separating stains so that stained regions can be detected (e.g. by setting a threshold), and then the number or areas of stained structures may be quantified. Two potential approaches would be: 1. If one measurement should be made for the entire image: - *Image > Adjust > Threshold...* - *Edit > Selection > Create Selection* - *Analyze > Measure* 2. If distinct structures should be measured: - *Image > Adjust > Threshold...* - *Analyze > Analyze Particles...*

An ImageJ macro for calculating empty surfaces on histological slices (ex: tubules in a kidney).

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

MembraneQuant performs automatic evaluation on IHC membrane stainings (HER2, EGFR etc).

Using color deconvolution, MembraneQuant detects cell membrane and measures staining intensity on the chromogen channel. This way it is possible to calibrate the software to the actual stain protocol in the pathology lab. The algorithm categorizes the detected membrane to weak positive, medium positive and strong positive classes.

This software has an IVD certification for HER2 quantification.