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Welcome to the documentation for ZarrNii, a Python library for working with OME-Zarr and NIfTI formats. ZarrNii bridges the gap between these two popular formats, enabling seamless data transformation, metadata preservation, and efficient processing of large biomedical images.

What is ZarrNii?

ZarrNii is designed for researchers and engineers working with:

  • OME-Zarr: A format for storing multidimensional image data, commonly used in microscopy.
  • NIfTI: A standard format for neuroimaging data.

ZarrNii allows you to:

  • Read and write OME-Zarr and NIfTI datasets.
  • Perform transformations like cropping, downsampling, and interpolation.
  • Preserve and manipulate metadata from OME-Zarr (e.g., axes, coordinate transformations, OME annotations).

Key Features

  • Seamless Format Conversion: Easily convert between OME-Zarr and NIfTI while preserving spatial metadata.
  • Transformations: Apply common operations like affine transformations, downsampling, and upsampling.
  • Multiscale Support: Work with multiscale OME-Zarr pyramids.
  • Metadata Handling: Access and modify OME-Zarr metadata like axes and transformations.
  • Lazy Loading: Leverage Dask arrays for efficient processing of large datasets.

Quick Start

from zarrnii import ZarrNii

# Load an OME-Zarr dataset
znimg = ZarrNii.from_ome_zarr("path/to/zarr_dataset.ome.zarr")

# Perform a transformation (e.g., downsample)
downsampled_znimg = znimg.downsample(level=2)

# Save as NIfTI
downsampled_znimg.to_nifti("output_dataset.nii")

Learn More

Explore the documentation to get started: