Clinical software module VirtualDoubleStaining™
for histopathology laboratoriesimage analysisdiagnostic

Clinical software module - VirtualDoubleStaining™  - Visiopharm - for histopathology laboratories / image analysis / diagnostic
Clinical software module - VirtualDoubleStaining™  - Visiopharm - for histopathology laboratories / image analysis / diagnostic
Clinical software module - VirtualDoubleStaining™  - Visiopharm - for histopathology laboratories / image analysis / diagnostic - image - 2
Clinical software module - VirtualDoubleStaining™  - Visiopharm - for histopathology laboratories / image analysis / diagnostic - image - 3
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Characteristics

Applications
clinical, for histopathology laboratories
Function
image analysis, diagnostic, interpretation
Type
automated

Description

Easy, Robust, and Automated Tumor/ Stroma Separation Avoid time-consuming manual outlining of tumor areas. VirtualDoubleStaining™ is a novel and patent method for automated, robust and verifiable tumor/stroma separation. It can be based on manual outlining as well as fully automated quantitative image analysis. The method has been developed and validated in collaboration with the NordiQC and thoroughly tested in practical clinical environments at major diagnostic pathology labs across Europe. Virtual Triple Staining The tumor marker slide can also be virtually aligned with two additional slides creating a sandwich around the tumor marker. Virtual Triple Staining allows analysis of even more biomarkers, giving you a fuller picture of the tumor microenvironment. Confidence in your clinical decisions Important clinical decisions are made by pathologists based on the assessment of biomarkers. It is well known that there is significant intra- and inter reader variability associated with diagnostic reading and interpretation of biomarker expression. There is an urgent need to reduce or eliminate this type of variability for biomarkers to be used as prognostic and predictive markers. Data from thousands of tissue samples supports that image analysis for tumor area detection, combined with quantification of biomarker expression, provide significant improvements in both reproducibility and accuracy.

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