Quantitative histology and tissue analysis

Tissue analysis

The automatic quantification of tissues serves to reproduce the interpretation of pathologists, by means of algorithms. The tissue sections are arranged on microscope slides and digitized with a slide scanner. Automatic quantification is a complex task because:

Images are huge: slide scanner generates virtual slides combining thousands of microscope fields (tiles) that correspond to the entire tissue sample
Slide to slide variability: the inclusion steps, staining steps and acquisitions steps can lead to important differences between identical slides. Thus it is important to process carefully images in order to reduces slide to slide differences. Internal normalization can help to standardize colors and intensities
Several segmentation steps are required to achieve good quantification: sample segmentation (to remove the background of the sample), tissue structure segmentation (like epithelium area, necrotic area, lumens, tissue lesions), cells segmentation (to obtain individual intensity, shape, number of spots)
Result visualization is not trivial: the visual inspection of slide quantification is very important and the user has to verify the results at different resolutions. He has to be able to navigate with ergonomic tools to zoom in, zoom out and pane the original image and superimpose resulting segmentation
In many situation, the analysis has to be guided by the expert or the anatomopathologist. They define region of interest, include or exclude areas according to their understanding. So it is important to combine automatic quantification with expert supervision

The tissue slides and Tissue Micro Array (TMA)

A tissue slide can contain :

Histological stainings

Common stainings are :

Histochemistry: staining based on chemical reactions between reagents and components of the tissue under study (e.g.: hematoxylin-eosin staining, Periodic Acid-Shiff (PAS), Masson’s Trichrome de Masson)

Immunohistochemistry (IHC): Immunological recognition of any given antigenic target (e.g. protein). Antibodies are coupled to an enzyme that convert a chromogenic substrate in a colored precipitate. Detection can be direct (enzyme-coupled primary antibody to antigen) or indirect (enzyme-coupled secondary antibody recognizes non-coupled primary antibody).

Immunofluorescence (IF): Immunological recognition of any given antigenic target (e.g. protein). Antibodies are coupled to a fluorochrome. Detection can be direct (fluorochrome-coupled primary antibody to antigen) or indirect (fluorochrome-coupled secondary antibody recognizes non-coupled primary antibody)

In situ hybridisation in situ (ISH): A probe recognizes a particular DNA or RNA sequence, which allows the localization or quantification of this sequence at the cellular level. The cDNA probe is labeled with an isotope or a fluorochrome (FISH)

Quantitative histological analyses

The quantitative information that is extracted from a tissue can be of different types:

Description of tissue morphology and organization
Description of cellular organization within a tissue
Localization and expression of biomarkers in a tissue of in cells

The gain of automated tissue analysis

Automated tissue analysis facilitates:

Analysis of a unlimited number of histological sections
Overall quantification of a slide
Quantification of each tissue structure
Precise quantification of each cell
The selection of unusual structures of interest
Easy navigation through the mass of images

3D Histology

To obtain 3D information in tissue analysis, sequential slides are cut and acquired to perform:

3D reconstruction of the object, organ, tissue structure (bio-materials, tumor, vessels)
Measure 3D volumes
3D visualization

3D histology is achieved using image registration tool boxes like ITK or Elastix.

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34090 Montpellier, France

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