omero-integration

To retrieve microscopy images and metadata from OMERO using Python, you can manage sessions, traverse the Project → Dataset → Image hierarchy, and fetch pixel planes as NumPy arrays via the OMERO Python API.

Yes, you can perform batch ROI analysis by creating ROIs, extracting shape statistics, and persisting quantitative results directly into OMERO tables for downstream analysis using the Python API.

Yes, high-content screening data structures are fully supported, allowing you to traverse the Screen → Plate → Well → WellSample hierarchy to navigate and process complex screening datasets.

The best way to generate derived images from microscopy pixel data is to fetch pixel planes as NumPy arrays, compute projections or rendered outputs, and create new images with correct physical dimensions.

To attach annotations and tags to datasets during automated microscopy pipelines, use the OMERO Python API to apply tags, map metadata, file annotations, and comments directly to images or datasets.

Yes, you can design OMERO.scripts for server-side batch processing of microscopy datasets, enabling automated workflows that return processed images, files, and tables directly to clients.