IHC Lung Cancer Mouse
Segment murine lung sections into tumor and non-cancerous tissue using a machine learning classifier, detect hematoxylin-stained nuclei, and quantify tumor area and marker-positive cellular phenotypes.
immunohistochemistry, lung cancer, tumor microenvironment, mouse, lung
The IHC Lung Cancer mouse App is using the machine learning base classifier to segment murine lung cancer tissue sections into tumor and non-cancerous tissue. Further it detects nuclei based on hematoxylin staining and identifies cellular phenotypes based on speicifc markers. It outputs number and area of tumor tissue as well as total number of cells and detected cellular phenotypes.
Emma Nye, Head of Experimental Histopathology, The Francis Crick Institute, London, UK
Original Image
Tumor and tissue detection
Nuclei detection
Phenotype detection
White Paper
17 Oct, 2025
Integrative Multiomics Approach Unveils Systemic Dysfunction in Colorectal Cancer (CRC)
White Paper
17 Oct, 2025
Integrative Multiomics Approach Unveils Systemic Dysfunction in Colorectal Cancer (CRC)
Blog Post
17 May, 2023
An Intro to Deep Learning in Biomedical Imaging
We support the following file formats:
- TissueFAXS (aqproj)
- StrataFAXS II (vmic)
- PreciPoint (vmic, gtif)
- Generic BigTIFF Import
- Support for multipage BigTIFF files
- OME-TIFF
- JPEG, PNG, BMP, TIFF
- Zeiss (czi)
- Hamamatsu NanoZoomer (ndpi)
- Aperio (svs)
- Leica (scn)
- 3D HISTECH Pannoramic
- Mirax (mrxs)
- Olympus (vsi)
- More slide scanners to be added!
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Custom App development
Perfectly tailored image analysis solutions for your research.
You have a specific research question that needs to be answered? We offer custom development of image analysis pipelines for specific tasks, be it detection of cellular phenotypes or quantification of tissue structures. After discussing your goals with one of our experts, you will get a ready-to-use App and be a step closer to an impactful publication.
