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inForm Tissue Finder

inForm® Tissue Finder adds exceptional functionality to inForm Cell Analysis to automate the detection and segmentation of specific tissues through powerful patented pattern recognition algorithms. Automation provides consistent reproducible results and enables comparative studies of multiple markers and specimens, supporting researchers to make faster discoveries of the indicators of disease.

Part Number CLS135783
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Detail Information

Once trained, inForm will locate and analyze user-specified regions automatically across an entire image or multiple images. Large numbers of images can be rapidly batch processed, allowing analysis that might have taken days to be done in a matter of minutes.

Key Features:

  • Visualizes, analyzes, quantifies and phenotypes immune and other cells in situ in solid tissue
  • Pathology Views to render immunofluorescence images as H&E, DAB and hematoxylin
  • Automated quantitation of biomarker expression from specific tissue types (e.g. tumor/stroma)
  • Trainable tissue segmentation learns by example – automatically locates tissues / structures of interest
  • Separates weakly expressing and overlapping markers
  • Batch processing of images using customizable image analysis workflows
  • Scoring (% positivity, 0/1+/2+/3+, co-localization and more)
  • Review and merge data from a set of images or slides into summary data files to assure data quality
  • Export data and images for further analysis e.g. TIBCO Spotfire® Analyst for Quantitative Pathology
  • Supports PerkinElmer’s Phenoptics workflow solution for cancer immunology research

Resources, Events & More
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Application Note

Application Note: Detecting Phenotypical Subgroups in Breast Cancer using Multiplexed Protein Expression Analysis in Intact Tissue Sections

Detecting Phenotypical Subgroups in Breast Cancer using Multiplexed Protein Expression Analysis in Intact Tissue Sections

Application Note: Phenotyping Immune Cell Subsets in situ in FFPE Tissue Sections

Cancer immunotherapy is rapidly,changing the landscape of cancer,treatment, with checkpoint inhibitors,such as those targeting CTLA-4 and PD-L1, activated T cell therapies and a range of,other combinatorial approaches providing significantly longer benefits for patients,compared to small molecule inhibitor and targeted therapy approaches1,2. However,response rates range from 15 – 30%, pointing to the need for the use of biomarkers for,better stratification. “A more complete understanding of the cellular and molecular,components of the tumor-immune system interaction is crucial to the development of,rational and efficacious immunotherapies in the future3.” While quantifying the number,of specific subsets of immune cells in blood is routine using multimarker flow cytometry,monitoring these same subsets of immune cells in solid tumors remains unobtainable,with standard methods. Methods that involve decomposing the tumor and releasing the,immune cells for flow cytometric analyses are difficult. Even when working properly with,no biasing of the cell populations, these methods cannot reveal the contextual spatial,relationships and the functional states of the immune cells within and around the tumor,which leading researchers today believe may contain vital information needed to predict,response to specific immunotherapies.


Product Information Bulletin


Scientific Paper