Digital Image Analysis - QuantCenter

Digital Image Analysis

QuantCenter is a powerful, automatic image analysis platform designed for digital whole slide quantification process.

Designed to fit seamlessly in the conventional microscopic investigation process, QuantCenter includes algorithms from tissue classification to cell-based FISH analysis that can be freely combined. It offers computer-aided image analysis allowing accurate, high-quality analytical results to be generated quickly.

 

The QuantCenter framework allows the connection of a variety of image analysis applications to generate a unique image analysis scenario. By using this feature, as the first step tissue classification modules can be applied to identify the region of interest (cancer regions), then a specific cell-based quantification module can detect the cancer cells and measure their morphometrical and intensity features.

 

The defined profiles can be saved and used for further analyisis. Applying batch analysis mode multiple digital slides can examine in the background and save you time. With the data visualization options, results can be viewed in a scatterplot, histogram, or pie chart. All of the measurement results can be exported into an Excel file.

 

View the applications and relevant image analysis modules below.



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Molecular Pathology

 

Molecular Pathology
FISHQuant
  • A powerful cancer and cytogenetic application dedicated to quantify FISH (Fluorescence In Sytu Hybridization) signals on tissue samples of solid tumor diseases like: breast and lung cancer, sarcomas, and lymphomas.
  • This module is suitable for examination of hematologycal tumors, FISHQuant classifies the interphase and metaphase cells individually for a comprehensive evaluation.
  CISHQuant
  • Quantify CISH (Chromogenic In Sytu Hybridization) stained samples. The algorithm can be calibrated to the stain protocoll and quality by using an integrated colour setting tool. This module is suitable for examining gene amplifi cation, deletion and chromosome aberration.

 

CISH-RNAQuant
  • Detects RNA virus in virus-infected cell nuclei (Epstein-Barr vírus, HPV, HHV8).
  • The application contains a colour adjustment module which can be calibrated to the applied stain protocol and quality.

Histopathology

 

Pannoramic DESK II

Tissue classification

HistoQuant
  • A histological segmentation module which identifies tissue elements based on the colour and intensity of the image pixels.
  • This module could be run as a standalone application or could be combined with any of our IHC quantification modules for brightfield or fluorescence analysis.
  PatternQuant
  • A trainable pattern recognition module for tissue classification, tissue pre-segmentation and identification of different tissue structures.
  • The machine-learning-based algorithm is able to classify different tissue types based on their texture pattern and colour features.

 

 

IHC Quantification

 

CaseCenter
NuclearQuant
  • A cell nuclei detection module designed for cell nuclei detection and quantification of IHC stained samples. The algorithm can be calibrated to the stain quality (local laboratory protocol or different stainer) by using an integrated colour setting tool.

 

MembraneQuant
  • A membrane detection software application can be used for IHC stained histological sample quantification. The algorithm can be calibrated to the stain quality (local laboratory protocol or different stainer) by using an integrated colour setting tool.
  CellQuant
  • A cell detection application which is optimal for several IHC quantification.
  • The application is adequate for cell nuclei, cytoplasmatic and membrane marker quantification. The software reports results based on dedicated scores and positivity ranges of cell nuclei, cytoplasm or membrane signals.

 

DensitoQuant
  • An easy to use, fast and accurate, stain-intensity-based IHC quantification tool.
  • The application identifies the positive stain, based on an automatic colour separation method through which individual positive pixels are counted and classified based on intensity and threshold ranges.

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