A collection of fractal tasks to convert HCS Plates to OME-Zarr
Project description
Fractal UZH Converters
A collection of Fractal tasks to convert High-Content Screening (HCS) plate data from various microscopes into OME-Zarr format.
Supported Microscopes
| Microscope | Manufacturer | Task Name |
|---|---|---|
| Operetta / Opera Phenix | Revvity | Convert Operetta Plate to OME-Zarr |
| ScanR | Evident | Convert Evident ScanR Plate to OME-Zarr |
| CQ3K | Yokogawa | Convert Yokogawa CQ3K Plate to OME-Zarr |
| CellVoyager | Yokogawa | Convert Yokogawa CellVoyager Plate to OME-Zarr |
| ImageXpress HCS.ai | Molecular Devices | Convert MD ImageXpress HCS.ai Plate to OME-Zarr |
| Custom TIFF (HCS plate) | Any | Convert Custom TIFF HCS Plate to OME-Zarr |
| Custom TIFF (single images) | Any | Convert Custom TIFF Images to OME-Zarr |
Each converter reads the microscope's native metadata and image files, then produces a well-structured OME-Zarr HCS plate.
Installation
pip install fractal-uzh-converters
How It Works
Each converter is implemented as a Fractal Compound Task consisting of two steps:
- Init task — Parses the microscope metadata, creates the OME-Zarr plate structure, and generates a parallelization list.
- Compute task — Reads the raw image tiles and writes them into the OME-Zarr dataset. This task runs in parallel across wells.
You configure the init task with one or more acquisitions (paths to your raw data directories) and the converter handles the rest.
Key Features
- Multiple tiling modes — Snap to grid, snap to corners, inplace, or no tiling depending on your acquisition layout.
- Condition tables — Attach experimental metadata (drug treatments, concentrations, replicates) to wells via a CSV file.
- Flexible writer modes — Choose between per-FOV, per-tile, Dask-parallel, or in-memory writing strategies to balance speed and memory usage.
- Overwrite control — No overwrite, full overwrite, or extend mode to incrementally add acquisitions.
- OME-NGFF 0.4 and 0.5 — Target either specification version for the output.
Documentation
Full documentation is available at: https://fractal-analytics-platform.github.io/fractal-uzh-converters/
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