Digital histology

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 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.

[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