MIKAIA^® Apps and AIs

Detecting positive and negative cells in nuclear IHC stainings: The app counts positive (DAB+) and negative (H+) cells in IHC stainings to calculate statistics, e.g., cell amount and cell density in cells/mm2. The app is designed to be compatible with a wide range of antigens, including nuclear or cytoplasmic antigens. It can also be used to detect distinctly stained cells in other non-IHC stains.

Single-cell analysis of immunofluorescence slides: The app enables single cell analysis of immunofluorescence and FISH slides, measures marker expression, and conducts differential ROI analysis. Phenotypes can be derived via user-defined cut-offs (supervised) or clustering (unsupervised). Generated objects can be postprocessed with the Cell-Cell-Connections or the Cellular Neighborhood App.

Selecting tissue areas based on color: This app is a useful tool for manually defining regions of interest. It creates masks by thresholding one (or multiple) color channels, such as masking chromogens in IHC scans. In fluorescent mIF scans, it can mask specific markers (or combinations) to generate a ROI for subsequent cell analysis using the FL Cell Analysis App.

AI based CISH analysis and grading: Supports chromogenic in situ hybridization workflows with AI driven cell segmentation and spot detection, enabling gene to cell mapping and classification of cells based on gene copy numbers or ISH ratios.

Counting cells and creating cell training sets: This “detection only” app version detects, segments, and outlines cells, without classifying them. Cells can be labeled by hand, e.g., using the class-changer brush.

Performing spatial analysis between cell types: The app interprets samples as graphs where cells are nodes, cell-cell connections are edges. Each cell connects to its adjacent cells, and these connections are classified based on the cell types involved. Optionally, long connections can be filtered. The results are visualized in a histogram and displayed in a matrix table.

Classifying cells by their cellular neighborhood: The app collects information about each cell’s k nearest neighboring cells and then clusters the per-cell neighborhood data.

Grouping adjacent cells or other annotation objects into clusters: The app outlines clusters and reports the number of objects contained in each cluster. Two adjacent cells are grouped into a cluster when their distance is less than a user-defined threshold. Additionally, a minimum number of objects per cluster can be required. Subsequently, clusters that contain less than the required amount of cells are removed.

Analyzing spatial layout and distribution: After running a tissue or cell AI, the app examines cell and tissue masks by overlaying a grid of tiles for measurements. It creates a histogram for all tiles, offering a comprehensive spatial profile that can be compared with other slides and correlated with specific endpoints.

Quantifying cell distances to target points: The app calculates distances from source annotations (e.g., immune cells) to target annotations (e.g., tumor invasive margin) and visualizes the shortest paths. It generates a distance histogram, offering insights into cell budding or invasive depth within tissue layers.

Genes‑to‑cell mapping and querying: The app associates gene‑specific spots from CISH, FISH, or spatial transcriptomics images with individual cells, enabling advanced queries based on gene copy numbers, presence of multiple genes, or gene counts per cell.

Ratio‑based FISH analysis: The app is an extension of the FL Cell AI / FISH App. It enables the AI based detection and segmentation of cells and gene signals, cell typing, and classification based on the ratio of two selected genes.

Iterating over existing annotations and calculating metrics: The app computes morphometric (e.g., area, perimeter) as well as color metrics (e.g., mean fluorescence intensity per channel) for a given set of manually or automatically generated annotations. It’s useful for IHC Profiling and for computing the mean fluorescence intensity (MFI) per annotation or TMA core.

Putting your AI into the hands of a pathologist: The MIKAIA^® Plugin API can be used to plug in your own Python script (or any other language). It communicates with MIKAIA^® via a REST API.