SMART SENSING insights

MIKAIA IHC Cell Quantification AI Gallery

This gallery illustrates for a range of different tissue types and IHC markers the cell analysis quality of MIKAIA‘s IHC Cell Detection App. The app first unmixes the stain components, then uses an AI to delineate cells, then computes the stains’ intensity per cell and finally classifies the cells into negative / positive (or low / medium / strong positive) based on the marker intensity.

Cells with a too low intensity, too small or large size, black color, or cells that are located in an “ignore” annotations can optionally be filtered out. Cells can also be grouped by ROI, e.g., the AI Author App can be used to recognize tumor vs non-tumor areas. The IHC Cell Detection App can then either be used to only analyze cells in the previously detected tumor region or it can analyze cells everywhere, but separate cell types based on what ROI (e.g., tumor or non-tumor) they are located in.

The IHC Cell Detection App also includes post-processing functionality, e.g., it can search hotspots, group positive cells into clusters, or create a heatmap. Additional apps offer further functionality, e.g., the Proximity Analysis App can be used to compute distances from cells to the tumor interface or between cell types.

Human Breast Her2 subcellular Human Breast Her2 subcellular with markup

CD3 Mouse brain


B220 Mouse Colon


B220 Mouse Colon (ROI 2)


IBA1 Mouse Brain


CK8 Mouse Brain


Ki67 Human Tongue


CD45 Human Tongue (ROI 1)


CD45 Human Tongue (ROI 2)


CD3 Human Colon


PGR Human Breast (overview)


PGR Human Breast (ROI 1)


Ki67 Human Breast


HER2 Human Breast (overview)


HER2 Human Breast (ROI 1)


HER2 Human Breast (ROI 2)


HER2 Human Breast (ROI 3)

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Volker Bruns

Volker is a digital pathology and spatial biology enthusiast with a computer science background. Volker and his team develop commercial image analysis software for digital pathology and offer contract development, as well as image analysis as a service in the life sciences.

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