ndevio

A generalized image format reader for napari built on top of bioio

ndevio provides flexible, metadata-aware image I/O for images in napari. Originally developed as part of napari-ndev, ndevio has since been separated into its own plugin as part of the ndev-kit and is intended to be a feature-rich and easier to maintain spiritual successor to napari-aicsimageio.

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Features

• Extensive format support via bioio and its plugin system — read OME-TIFF, OME-Zarr, common image and movie formats, proprietary formats (CZI, LIF, ND2), and many more (with bioformats)!
• Multi-scene handling — interactive widget for selecting between scenes/positions in multi-scene files
• Configurable behavior via ndev-settings — customize reader priority, multi-scene handling, and more
• Smart plugin installation — automatic suggestions to install missing bioio reader plugins
• Programmatic API — nImage class for napari-ready metadata extraction
• Batch utilities — legacy widget for batch concatenation (with nbatch) and metadata management, with features being superseded by napari-metadata
• Sample data — demonstrates ndevio metadata handling and capabilities

Installation

You can install ndevio from PyPI or via the napari plugin manager:

pip install ndevio

If you would like to try out ndevio, you can run napari in a temporary environment with uv:

uvx --with ndevio -p 3.13 "napari[all]"

To contibute to ndevio or experiment with the latest features, see Contributing.md for development setup instructions. Conda-forge availability is coming soon!

Additional Image Format Support

ndevio uses bioio for flexible image reading. Basic formats (TIFF, OME-TIFF, OME-Zarr, PNG, etc.) are supported out of the box via:

• bioio-ome-tiff - OME-TIFF files
• bioio-ome-zarr - OME-Zarr files
• bioio-tifffile - General TIFF files
• bioio-imageio - PNG, JPEG, and other common formats

If your image format is not supported by the default readers, then you will get a warning and (by default in napari) a widget to install the suggested reader. If you know of your additional proprietary formats, install the appropriate bioio reader. See the bioio documentation for the full list of available readers. Note: The use of bioio-bioformats has not been fully tested and may have issues. Please file an issue if you encounter problems.

Usage

In napari

Simply drag and drop image files into napari. ndevio handles the rest! To learn more about the decisions that ndevio (and its settings) makes when loading images, see How ndevio Handles Images below.

Multi-scene Images

When opening multi-scene files (e.g., multi-position acquisitions, mosaics), a Scene Widget appears in the viewer, allowing you to select which scene to display. Configure default behavior via the Settings widget.

Bioio Reader Plugin Installation Widget

If you open a file that requires a bioio reader not currently installed, ndevio will display a BioIO Plugin Installation widget in napari suggesting the appropriate plugin to install.

This widget taps into the napari-plugin-manager to install the bioio reader plugin from PyPI via a GUI. You may invoke this widget manually at any time via Plugins > ndevio > Install BioIO Reader Plugins to install any additional bioio reader plugin and update any currently installed plugins.

Settings Widget

Access ndevio settings via Plugins > ndev-settings > Settings to customize:

• Preferred reader: Override bioio's default plugin selection priority (useful for formats with multiple compatible readers)
• Multi-scene handling: Choose whether to show the scene widget, view all scenes as a stack, or view only the first scene
• Plugin suggestions: Enable/disable automatic plugin installation prompts for unsupported formats

These settings are managed by ndev-settings and persist across napari sessions.

Utilities Widget

Access via Plugins > ndevio > Utilities for:

• Batch concatenation of images
• Metadata management
• Export as OME-TIFF or figure (PNG)

Note: Elements of this widget are being superseded by napari-metadata for more comprehensive metadata handling. Note 2: This widget was built during napari-ndev mono-repo development and does not fully reflect the design goals of ndevio. It will remain functional, but expect future versions to look different.

Programmatic Usage with nImage

The nImage class extends bioio's BioImage with napari-specific functionality:

from ndevio import nImage
from napari import Viewer

# Load image with automatic metadata extraction
img = nImage("path/to/image.czi")

# Because nImage subclasses BioImage, all BioImage methods are available
img.dims               # e.g., <Dimensions [T: 15, C: 4, Z: 1, Y: 256, X: 256]>

# Access napari-ready properties, note that channel and singleton dimensions are dropped
img.layer_data.shape   # e.g. (15, 4, 256, 256) - still includes channel dimbecause it has not yet been converted to a list of LayerDataTuples
img.layer_scale        # e.g., (1.0, 0.2, 0.2) - time interval + physical scale per dimension, napari ready
img.layer_axis_labels  # e.g., ('T', 'Y', 'X')
img.layer_units        # e.g., ('s', 'µm', 'µm')
img.layer_metadata     # e.g., a dictionary containing the 1) full BioImage object, 2) raw_image metadata and 3) OME metadata (if parsed) - accessible via `viewer.layers[n].metadata`

# A convenience method to get napari LayerDataTuples with nImage metadata for napari
viewer = Viewer()
for ldt in img.get_layer_data_tuples():
    viewer.add_layer(ldt)

Sample Data

ndevio includes sample datasets accessible via File > Open Sample > ndevio. These samples use the nImage API to demonstrate the metadata handling.

• 2D neural cells in a dish imaged with 4 channels, with corresponding segmentation labels
• 2D brain slice with 3 different transcription factor antibody stains
• A single 2D+Time sample from a scratched retinal epithelial cell culture, including auto-detected labels in the same file.
• the napari-ndev logo as a .png

How ndevio Handles Images

Metadata

Image metadata is extracted from bioio and converted to napari layer metadata based on the current napari convention to squeeze out singleton dimensions (i.e. drop dimensions of size 1):

• Full metadata available at viewer.layers[n].metadata
• Time and physical scale automatically applied to layers from OME metadata or file headers
• Dimension labels (T, C, Z, Y, X) preserved in axis_labels
• Physical units (µm, nm, etc.) stored in layer metadata

Memory Management

Images are loaded in-memory or lazily (via dask) automatically based on:

• File size < 4 GB AND
• File size < 30% of available RAM

Multi-channel Images

Multi-channel images are always split into individual layers (one per channel), using channel names from metadata when available. Images are added with colorblind-friendly colormaps.

Mosaic/Tiled Images

Images with tiles (e.g., stitched acquisitions) are automatically stitched together if the reader supports this behavior.

RGB Images

RGB(A) images are currently added to the viewer as a single RGB(A) layer, according to napari conventions. RGB(A) images are identified by containing the Samples ('S') dimension by bioio, which nominally exists for images with the last (-1) dimension being of size 3 or 4.

Detection of Labels/Segmentation Layers

If an image contains a channel name or file name suggestive of a labels layer, ndevio will add that channel as a labels layer in napari. Mixed image and label files are possible by having information in the channel names (e.g., ["DAPI", "DAPI-labels"]).

Customization

If you need different behavior for any of these automated handling rules, please file an issue — we may be able to add settings to configure them!

Coming Soon

Writers for OME-TIFF and OME-Zarr with round-trip napari metadata support!

Contributing

Contributions are very welcome! Please see Contributing.md for development setup and guidelines.

License

Distributed under the terms of the BSD-3 license, "ndevio" is free and open source software

Issues

If you encounter any problems, please file an issue along with a detailed description.