Overview

This repository provides a modular framework for automated image acquisition and adaptive fluorescence spectroscopy experiments on confocal microscopes (Zeiss LSM series with ZenBlue).

The framework integrates directly with the Zen Blue graphical user interface, enabling full microscope control without leaving the GUI environment.

The software was developed to support:

  • Automated imaging and fluorescence correlation techniques (FCS, RICS)

  • Robust object detection and re-analysis

  • Adaptive measurement workflows based on object properties

  • Reproducible data organization and processing

Although originally designed for specific experimental setups, the framework is highly modular and can be adapted to a wide range of microscopy workflows.

For full documentation, see: ZeissAutomation documentation

Key Features

  • Automated acquisition pipelines for imaging and spectroscopy

  • Grid-based overview scanning for rapid sample mapping

  • Object detection and re-analysis in XY and Z dimensions

  • Adaptive experiments triggered by detected object properties

  • Automatic data organization using unique identifiers (UUIDs)

Typical Workflow

A typical experiment follows these steps:

  1. Generate an overview grid defining measurement positions

  2. Prepare .czexp experiment files describing acquisition settings

  3. Acquire overview images and detect objects of interest

  4. Re-analyze object positions to compensate for stage inaccuracies and object movement

  5. Perform Z-scans to locate signal maxima

  6. Trigger downstream experiments by specifying the corresponding .czexp files (e.g. RICS, FCS, time-lapse)

  7. Dynamically modify experiments based on object-specific properties

  8. Automatically store and organize all results

Architecture

The framework is organized around several core components:

ZeissAPI

Provides microscope control via the Zen Blue API using .czmac macros. Handles GUI integration and Python subprocess execution.

data_processing

Contains data processors responsible for image analysis and generation of JSON files describing object positions and properties, which are then consumed by macros.

Processors

  • ZeissImageProcessor – image analysis and object detection

  • ZeissFCSProcessor – fluorescence correlation spectroscopy analysis

This modular architecture ensures that the codebase remains flexible, maintainable, and extensible.

Adaptive Measurements

A key feature of the framework is support for adaptive experiments.

Adaptive behavior is defined using a simple dictionary structure that maps the name of a .czexp experiment to a Python function responsible for modifying the experiment parameters:

{
    "experiment_name": adaptive_function
}

This mechanism allows experiments to be adjusted dynamically based on detected object properties or intermediate measurement results.