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Welcome to the documentation for ZarrNii, a Python library for working with OME-Zarr, NIfTI, and Imaris formats. ZarrNii bridges the gap between these 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.
  • Imaris: A microscopy file format (.ims) using HDF5 structure for 3D/4D image analysis.

ZarrNii allows you to:

  • Read and write OME-Zarr, NIfTI, and Imaris datasets.
  • Work with 4D and 5D images, including time-series data (T,C,Z,Y,X).
  • Perform transformations like cropping, downsampling, and interpolation.
  • Select specific channels and timepoints from multidimensional datasets.
  • Preserve and manipulate metadata from OME-Zarr (e.g., axes, coordinate transformations, OME annotations).

Key Features

  • Seamless Format Conversion: Easily convert between OME-Zarr, NIfTI, and Imaris while preserving spatial metadata.
  • 5D Image Support: Work with time-series data in (T,C,Z,Y,X) format with timepoint and channel selection.
  • 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.
  • Segmentation Plugins: Extensible plugin architecture for image segmentation algorithms.

Quick Start

from zarrnii import ZarrNii

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

# Or load from Imaris (requires optional dependency)
# znimg = ZarrNii.from_imaris("path/to/microscopy_data.ims")

# Load with specific timepoints and channels (5D support)
znimg_subset = ZarrNii.from_ome_zarr("timeseries.zarr", timepoints=[0, 2], channels=[1])

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

# Apply segmentation using Otsu thresholding
segmented_znimg = znimg.segment_otsu(nbins=256)

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

Learn More

Explore the documentation to get started: