imaging-mass-cytometry

To segment cells in imaging mass cytometry data, you input marker images and segmentation masks to generate cell tables and quantify marker expressions across the tissue section. The workflow processes these inputs to extract cell-level features.

Phenotyping cells and mapping spatial patterns from tissue imaging involves assigning phenotype labels to segmented cells and summarizing neighborhood distributions. This generates tissue-level spatial maps and phenotype summaries across diverse marker panels.

Yes, you can use a Python-based imaging workflow to analyze multiplexed imaging mass cytometry data. It processes diverse marker panels using image analysis utilities to input marker images, panel metadata, and segmentation masks for cell-level analysis.

To generate cell tables and spatial summaries from IMC tissue imaging, you need marker images, panel metadata, and segmentation masks. These inputs allow the workflow to output cell tables, phenotype assignments, and spatial maps.

You need multiplexed imaging mass cytometry segmentation and spatial analysis when deriving cell states and neighborhood patterns from tissue sections. It is applicable for tissue imaging studies requiring cell-level features, phenotype assignments, and spatial summaries.