ZenReg: Fast and memory-efficient N-dimensional microscopy image registration for Python

ZenReg is a Python package for modular microscopy image registration. It is designed for time-resolved, volumetric, and multi-channel microscopy data and uses a canonical TZCYX data model:

T = time
Z = z slices
C = channels
Y = image rows
X = image columns

The main workflow is intentionally short:

from zenreg import load_stack, register_stack, save_stack

image, metadata = load_stack("image.ome.tif", return_metadata=True)

registered, details = register_stack(
    image,
    registration_channel=0,
    method="phase_cross_correlation",
    return_shifts=True,
    return_details=True)

save_stack(
    "image_registered.ome.tif",
    registered,
    metadata=metadata,
    registration_details=details)

ZenReg writes registered OME-TIFF files together with optional CSV, YAML, and PNG report sidecars so registration settings and quality-control outputs remain reproducible and easy to share.

Example image registration before and after ZenReg

What ZenReg is for

ZenReg is built for common microscopy registration tasks:

• 2D+t time-lapse registration with global XY motion.
• 2D+t in-plane rotation correction.
• 3D+t registration using fast Z projections or full ZYX volumes.
• 3D and 3D+t intra-stack XY slice correction.
• Full 3D rigid 6-DOF registration for structural volumes.
• NoRMCorre-style rigid and piecewise-rigid correction.
• Multi-channel registration where one channel is used for estimating motion and all channels are transformed consistently.
• Memory-efficient workflows for large microscopy files through OMIO-backed disk caches.

Supported inputs

ZenReg uses OMIO for microscopy I/O. OMIO normalizes supported inputs to canonical TZCYX arrays and returns a metadata dictionary that ZenReg carries through to registered outputs.

Supported formats include:

• TIFF and OME-TIFF
• CZI
• LSM
• Thorlabs RAW

Registration methods

ZenReg provides several registration backends through the same register_stack wrapper:

Memory-efficient processing

Large microscopy stacks can be read through OMIO disk-backed Zarr caches:

from zenreg import cleanup_omio_cache, load_stack

memmap_folder = "/path/to/local/omio_cache"

image, metadata = load_stack(
    "large_server_file.ome.tif",
    return_metadata=True,
    use_memmap=True,
    memmap_folder=memmap_folder,
    memmap_reuse=True)

This is useful when files are larger than available RAM or when raw data live on a server or network volume. A local cache lets ZenReg process chunked data from fast local storage and reuse an existing validated cache across repeated parameter tuning sessions. Cache cleanup is explicit:

cleanup_omio_cache(memmap_folder, full_cleanup=True)

Installation

ZenReg requires Python 3.12 or newer. The package has been tested with Python 3.12.

Create a fresh environment:

conda create -n zenreg -y python=3.12
conda activate zenreg

Install from PyPI:

pip install zenreg

Verify the installation:

python -c "import zenreg; print(f'ZenReg {zenreg.__version__} imported successfully; available CPUs: {zenreg.available_cpu_count()}')"

For a development checkout:

git clone https://github.com/FabrizioMusacchio/ZenReg.git
cd ZenReg
pip install -e ".[dev,docs]"

Synthetic tutorial data

The tutorials use synthetic OME-TIFF datasets with matching ground-truth CSV tables. Generate them with:

python additional_scripts/create_synthetic_example_data.py

This writes datasets into example_data/synthetic_data/. The repository keeps example_data/ as the tutorial data location, but the generated image data are not intended to be committed.

Useful tutorial scripts:

• user_scripts/register_synthetic_examples_interactive.py
• user_scripts/register_normcorre_synthetic_examples.py
• user_scripts/register_rigid3d_synthetic_examples.py
• user_scripts/register_batch_bids_like_synthetic.py
• user_scripts/profile_zenreg_memory_synthetic.py

The scripts are structured with # %% cells for VS Code, Spyder, Jupyter-like interactive execution, and napari inspection.

Output files

When registration_details are passed to save_stack, ZenReg writes:

• *_registered.ome.tif: registered image with updated OMIO metadata.
• *_registration_shifts.csv: detected shifts, optional rotations, optional intra-stack shifts, and Pearson correlations before/after registration.
• *_registration_settings.yaml: settings and metadata needed to reproduce the registration.
• *_registration_summary.png: detected motion and correlation summary plot.

Additional tutorial helpers such as show_before_after, show_timepoints, show_slices, and open_in_napari support visual quality control during interactive analysis.

Documentation

The full documentation is available on Read the Docs:

https://zenreg.readthedocs.io/

It includes installation instructions, synthetic data generation, 2D+t and 3D+t registration tutorials, memory-efficient workflows, projection strategies, napari inspection, result assessment, full 3D rigid registration, and batch processing examples.

License

ZenReg is distributed under the terms of the GNU General Public License v3.0 or later. See LICENSE for details.

Citation

If you use ZenReg in scientific work, please cite the ZenReg preprint:

Musacchio, F., & Fuhrmann, M. (2026). ZenReg: A modular Python platform for fast and memory-efficient N-dimensional microscopy image registration. bioRxiv 2026.08.07.743572. https://doi.org/10.64898/2026.08.07.743572

If you need to cite the exact ZenReg software version used in your analysis, please also cite the corresponding Zenodo archive:

Musacchio, F. (2026). ZenReg: Fast and memory-efficient N-dimensional microscopy image registration for Python. Zenodo. https://doi.org/10.5281/zenodo.21727826