omio-microscopy 0.2.1

OMIO: Open Microscopy Image I/O

OMIO: A policy-driven Python library for reproducible microscopy image I/O

OMIO (Open Microscopy Image I/O) is a policy-driven Python library for reading, organizing, merging, visualizing, and exporting multidimensional microscopy image data under explicit OME-compliant axis and metadata semantics.

OMIO is designed as an infrastructure layer between heterogeneous microscopy file formats and downstream analysis or visualization workflows. It provides a unified I/O interface that enforces consistent axis ordering, metadata normalization, and memory-aware data handling across NumPy, Zarr, Dask, napari, and OME-TIFF.

NOTE: OMIO is currently under active development. The API and feature set may change in future releases. We also welcome feedback, feature requests, and contributions via GitHub issues and pull requests. Please report any bugs or inconsistencies you encounter.

A comprehensive documentation website is available at: https://omio.readthedocs.io.

What is OMIO good for your microscopy data?

Modern microscopy workflows face a recurring and largely unsolved problem: While image acquisition formats are diverse, downstream analysis and visualization pipelines implicitly assume consistent data semantics.

In practice, this leads to:

• ambiguous or undocumented axis conventions,
• silent shape mismatches across files,
• inconsistent or partially missing physical metadata,
• ad-hoc merge scripts that fail for large data,
• format-specific reader logic leaking into analysis code,
• and brittle visualization workflows for large volumetric or time-series data.

OME-TIFF and OME-XML define a powerful metadata standard, but most real-world microscopy data do not arrive in a clean, OME-conform form. Instead, users are left to bridge the gap manually, often repeatedly and inconsistently.

OMIO addresses this gap by acting as a semantic I/O layer rather than a format converter.

It provides:

• explicit and enforced axis semantics,
• structured metadata normalization,
• controlled merge and padding policies,
• memory-efficient handling of large datasets,
• and a consistent interface for both batch conversion and interactive visualization.

Installation

OMIO requires Python 3.12 or newer and can be installed via pip from the Python Package Index (PyPI):

conda create -n omio_env python=3.12 -y
conda activate omio_env
pip install omio-microscopy

Design principles

OMIO is built around the following principles:

Explicit axis semantics

All image data are handled internally using explicit axis strings (default: TZCYX).
Axis order is never implicit and never guessed silently.

OME-aware, but not OME-exclusive

OME semantics are used as the internal reference model, but OMIO is not restricted to OME-TIFF input or output.
OME-TIFF is treated as one well-defined sink among several possible representations.

Policy-driven behavior

Operations such as merging, padding, and metadata reconciliation are governed by explicit, documented policies rather than hidden heuristics.

Memory-aware by construction

Large datasets can be processed via Zarr and Dask without loading entire volumes into RAM.
Chunk-aligned copying and cache-based workflows are first-class concepts and allow both memory-mapped and out-of-core processing as well as memory-efficient visualization in napari.

Separation of concerns

Reading, merging, visualization, and writing are distinct stages that can be composed but are not entangled.

Core features

Unified image reading

OMIO provides a single entry point for reading microscopy image data. At the moment, supported formats include:

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

All readers return:

• an image object (NumPy array or Zarr array),
• a normalized metadata dictionary,
• and an explicit axis specification.

Metadata normalization and enforcement

OMIO normalizes metadata into a consistent dictionary that includes:

• axis string (axes)
• full 5D shape (TZCYX)
• physical pixel sizes (PhysicalSizeX/Y/Z)
• temporal resolution (TimeIncrement)
• units
• structured provenance stored via OME MapAnnotations

Non-OME metadata are preserved and stored explicitly inside annotations rather than discarded.

Controlled merging along semantic axes

OMIO supports concatenation and merging along semantic axes:

• Time (T)
• Depth (Z)
• Channel (C)

Merge behavior is configurable:

• strict compatibility checks, or
• zero-padding of non-merge axes to maximal extents.

All merges propagate provenance information into metadata annotations.

Image compression

OMIO supports reading and writing compressed image data. When writing OME-TIFF files, users can choose a zlib compression level between 0 (no compression) and 9 (maximum compression). I.e, OMIO can be used to shrink large uncompressed TIFF files into compressed OME-TIFF files, which facilitates significant disk space savings, faster I/O performance, and better transfer speeds.

Folder-based and BIDS-like workflows

OMIO supports structured folder traversal for large projects, including:

• reading all files in a folder,
• merging multiple files within a folder,
• merging structured "folder stacks" (e.g. repeated acquisitions),
• batch processing of BIDS-like directory hierarchies.

These workflows are designed to reflect how microscopy data are actually organized, not how idealized examples look.

OME-TIFF export

OMIO can write OME-TIFF files with:

• correct axis order,
• correct physical and temporal metadata,
• optional BigTIFF handling for large datasets,
• embedded MapAnnotations for provenance and custom metadata.

Filename handling is collision-safe and metadata-aware.

Napari integration

OMIO provides direct integration with napari, supporting:

• NumPy-based visualization for small data,
• Zarr-backed visualization for large data,
• optional Dask-based slicing and caching,
• correct spatial scaling and channel handling.

Axis squeezing and cache generation are performed explicitly and transparently.

Creating metadata and image templates

OMIO provides utility functions to create empty metadata and image templates that can be populated programmatically or used as blueprints for new datasets.

Expected project structure (BIDS-like)

OMIO supports batch processing of projects organized in a BIDS-like manner.
An abstract example is shown below:

project_name/
├─ sub-01/
│  ├─ exp_TP001/
│  │  ├─ image.tif
│  │
│  ├─ exp_TP002/
│  │  ├─ TAG_01/
│  │  │  ├─ image1.czi
│  │  ├─ TAG_02/
│  │  │  ├─ image2.czi
│  │
│  ├─ exp_TP003/
│  │  ├─ image1.tif
│  │  ├─ image2.tif
│
├─ sub-02/
│  ├─ exp_TP001/
│  │  ├─ image.tif

OMIO detects subjects, experiments, and optional tag folders via user-defined matching rules and applies consistent read and merge policies across the project.

Typical usage patterns

OMIO's core functions are:

• imread(): read images from files or folders,
• imwrite(): write images to OME-TIFF,
• imconvert(): convert images to OME-TIFF,
• bids_batch_convert(): batch-convert BIDS-like projects,
• open_in_napari(): visualize images in napari,
• create_empty_metadata(): create empty metadata templates,
• create_empty_image(): create empty image arrays, and
• update_metadata_from_image(): update metadata based on image properties.

Read a single file

img, metadata = omio.imread("image.czi")

Read all images in a folder

images, metadatas = omio.imread("experiment_folder", return_list=True)

Merge multiple files along time

merged_img, merged_md = omio.imread(
    "experiment_folder",
    merge_multiple_files_in_folder=True,
    merge_along_axis="T")

Convert to OME-TIFF

omio.imconvert("experiment_folder")

Batch conversion over a BIDS-like project

omio.bids_batch_convert(
    fname="project_root",
    sub="sub-",
    exp="TP",
    tagfolder="TAG_",
    merge_tagfolders=True)

Scope and non-goals

OMIO intentionally does not:

• perform image processing or analysis,
• infer or guess missing metadata silently,
• replace domain-specific analysis pipelines.

Its purpose is to provide a reliable, explicit, and reproducible I/O layer on which such pipelines can be built.

Currently supported file formats and dependencies

At the moment, OMIO supports reading the following microscopy file formats:

• TIF, ImageJ TIFF, OME-TIFF, multi file OME-TIFF series (*.tif, *.tiff, *.ome.tif, *.ome.tiff)
• Zeiss LSM (*.lsm)
• Zeiss CZI (*.czi)
• Thorlabs RAW binary files incl. Experiment.xml (*.raw + *.xml)

OMIO relies on the following third-party libraries for file format handling:

• tifffile, czifile, numpy, zarr, dask, napari, tqdm, pyyaml, imagecodecs, numcodecs

Requests for new file formats and reader extensions

OMIO is designed to grow and evolve based on user needs and real-world microscopy data. By intention, we will add support for additional file formats or format variants over time, bases on user requests and contributions.

Requests for new readers or reader extensions are therefore very welcome and should be submitted via the GitHub issue tracker. To be actionable, such requests must include access to a representative example file, as OMIO intentionally avoids speculative or heuristic-based format inference.

Detailed guidelines for requesting new format support, including what information to provide and how example files can be shared, are described in CONTRIBUTING.md.

License

OMIO is released under the GNU General Public License v3.0 (GPLv3). Please refer to the LICENSE file for details.

Citation

If you use OMIO in scientific work, please cite it as:

Fabrizio Musacchio. (2025). OMIO: A policy-driven Python library for reproducible microscopy image I/O. Zenodo. https://doi.org/10.5281/zenodo.18030883

On Zenodo, you can select other citation formats as needed as well as a DOI for dedicated OMIO software versions.

Acknowledgments

OMIO was developed to support real-world microscopy workflows where data heterogeneity, scale, and metadata inconsistencies are the norm rather than the exception.