Image Reading, Metadata Conversion, and Image Writing for Microscopy Images in Pure Python

• Supports reading metadata and imaging data for:

  • OME-TIFF
  • TIFF
  • ND2 -- (pip install aicsimageio[nd2])
  • DV -- (pip install aicsimageio[dv])
  • CZI -- (pip install aicspylibczi>=3.1.1 fsspec>=2022.8.0)
  • LIF -- (pip install readlif>=0.6.4)
  • PNG, GIF, etc. -- (pip install aicsimageio[base-imageio])
  • Files supported by Bio-Formats -- (pip install aicsimageio bioformats_jar) (Note: requires java and maven, see below for details.)

• Supports writing metadata and imaging data for:

  • OME-TIFF
  • PNG, GIF, etc. -- (pip install aicsimageio[base-imageio])

• Supports reading and writing to fsspec supported file systems wherever possible:

  • Local paths (i.e. my-file.png)
  • HTTP URLs (i.e. https://my-domain.com/my-file.png)
  • s3fs (i.e. s3://my-bucket/my-file.png)
  • gcsfs (i.e. gcs://my-bucket/my-file.png)

  See Cloud IO Support for more details.

Stable Release: pip install aicsimageio
Development Head: pip install git+https://github.com/AllenCellModeling/aicsimageio.git

AICSImageIO is supported on Windows, Mac, and Ubuntu. For other platforms, you will likely need to build from source.

TIFF and OME-TIFF reading and writing is always available after installing aicsimageio, but extra supported formats can be optionally installed using [...] syntax.

• For a single additional supported format (e.g. ND2): pip install aicsimageio[nd2]
• For a single additional supported format (e.g. ND2), development head: pip install "aicsimageio[nd2] @ git+https://github.com/AllenCellModeling/aicsimageio.git"
• For a single additional supported format (e.g. ND2), specific tag (e.g. v4.0.0.dev6): pip install "aicsimageio[nd2] @ git+https://github.com/AllenCellModeling/aicsimageio.git@v4.0.0.dev6"
• For faster OME-TIFF reading with tile tags: pip install aicsimageio[bfio]
• For multiple additional supported formats: pip install aicsimageio[base-imageio,nd2]
• For all additional supported (and openly licensed) formats: pip install aicsimageio[all]
• Due to the GPL license, LIF support is not included with the [all] extra, and must be installed manually with pip install aicsimageio readlif>=0.6.4
• Due to the GPL license, CZI support is not included with the [all] extra, and must be installed manually with pip install aicsimageio aicspylibczi>=3.1.1 fsspec>=2022.8.0
• Due to the GPL license, Bio-Formats support is not included with the [all] extra, and must be installed manually with pip install aicsimageio bioformats_jar. Important!! Bio-Formats support also requires a java and mvn executable in the environment. The simplest method is to install bioformats_jar from conda: conda install -c conda-forge bioformats_jar (which will additionally bring openjdk and maven packages).

For full package documentation please visit allencellmodeling.github.io/aicsimageio.

If your image fits in memory:

from aicsimageio import AICSImage # Get an AICSImage object img = AICSImage("my_file.tiff") # selects the first scene found img.data # returns 5D TCZYX numpy array img.xarray_data # returns 5D TCZYX xarray data array backed by numpy img.dims # returns a Dimensions object img.dims.order # returns string "TCZYX" img.dims.X # returns size of X dimension img.shape # returns tuple of dimension sizes in TCZYX order img.get_image_data("CZYX", T=0) # returns 4D CZYX numpy array # Get the id of the current operating scene img.current_scene # Get a list valid scene ids img.scenes # Change scene using name img.set_scene("Image:1") # Or by scene index img.set_scene(1) # Use the same operations on a different scene # ...

The .data and .xarray_data properties will load the whole scene into memory. The .get_image_data function will load the whole scene into memory and then retrieve the specified chunk.

If your image doesn't fit in memory:

from aicsimageio import AICSImage # Get an AICSImage object img = AICSImage("my_file.tiff") # selects the first scene found img.dask_data # returns 5D TCZYX dask array img.xarray_dask_data # returns 5D TCZYX xarray data array backed by dask array img.dims # returns a Dimensions object img.dims.order # returns string "TCZYX" img.dims.X # returns size of X dimension img.shape # returns tuple of dimension sizes in TCZYX order # Pull only a specific chunk in-memory lazy_t0 = img.get_image_dask_data("CZYX", T=0) # returns out-of-memory 4D dask array t0 = lazy_t0.compute() # returns in-memory 4D numpy array # Get the id of the current operating scene img.current_scene # Get a list valid scene ids img.scenes # Change scene using name img.set_scene("Image:1") # Or by scene index img.set_scene(1) # Use the same operations on a different scene # ...

The .dask_data and .xarray_dask_data properties and the .get_image_dask_data function will not load any piece of the imaging data into memory until you specifically call .compute on the returned Dask array. In doing so, you will only then load the selected chunk in-memory.

Read stitched data or single tiles as a dimension.

Readers that support mosaic tile stitching:

• LifReader
• CziReader

If the file format reader supports stitching mosaic tiles together, the AICSImage object will default to stitching the tiles back together.

img = AICSImage("very-large-mosaic.lif") img.dims.order # T, C, Z, big Y, big X, (S optional) img.dask_data # Dask chunks fall on tile boundaries, pull YX chunks out of the image

This behavior can be manually turned off:

img = AICSImage("very-large-mosaic.lif", reconstruct_mosaic=False) img.dims.order # M (tile index), T, C, Z, small Y, small X, (S optional) img.dask_data # Chunks use normal ZYX

If the reader does not support stitching tiles together the M tile index will be available on the AICSImage object:

img = AICSImage("some-unsupported-mosaic-stitching-format.ext") img.dims.order # M (tile index), T, C, Z, small Y, small X, (S optional) img.dask_data # Chunks use normal ZYX

If the file format reader detects mosaic tiles in the image, the Reader object will store the tiles as a dimension.

If tile stitching is implemented, the Reader can also return the stitched image.

reader = LifReader("ver-large-mosaic.lif") reader.dims.order # M, T, C, Z, tile size Y, tile size X, (S optional) reader.dask_data # normal operations, can use M dimension to select individual tiles reader.mosaic_dask_data # returns stitched mosaic - T, C, Z, big Y, big, X, (S optional)

There are functions available on both the AICSImage and Reader objects to help with single tile positioning:

img = AICSImage("very-large-mosaic.lif") img.mosaic_tile_dims # Returns a Dimensions object with just Y and X dim sizes img.mosaic_tile_dims.Y # 512 (for example) # Get the tile start indices (top left corner of tile) y_start_index, x_start_index = img.get_mosaic_tile_position(12)

from aicsimageio import AICSImage # Get an AICSImage object img = AICSImage("my_file.tiff") # selects the first scene found img.metadata # returns the metadata object for this file format (XML, JSON, etc.) img.channel_names # returns a list of string channel names found in the metadata img.physical_pixel_sizes.Z # returns the Z dimension pixel size as found in the metadata img.physical_pixel_sizes.Y # returns the Y dimension pixel size as found in the metadata img.physical_pixel_sizes.X # returns the X dimension pixel size as found in the metadata

If aicsimageio finds coordinate information for the spatial-temporal dimensions of the image in metadata, you can use xarray for indexing by coordinates.

from aicsimageio import AICSImage # Get an AICSImage object img = AICSImage("my_file.ome.tiff") # Get the first ten seconds (not frames) first_ten_seconds = img.xarray_data.loc[:10] # returns an xarray.DataArray # Get the first ten major units (usually micrometers, not indices) in Z first_ten_mm_in_z = img.xarray_data.loc[:, :, :10] # Get the first ten major units (usually micrometers, not indices) in Y first_ten_mm_in_y = img.xarray_data.loc[:, :, :, :10] # Get the first ten major units (usually micrometers, not indices) in X first_ten_mm_in_x = img.xarray_data.loc[:, :, :, :, :10]

See xarray "Indexing and Selecting Data" Documentation for more information.

File-System Specification (fsspec) allows for common object storage services (S3, GCS, etc.) to act like normal filesystems by following the same base specification across them all. AICSImageIO utilizes this standard specification to make it possible to read directly from remote resources when the specification is installed.

from aicsimageio import AICSImage # Get an AICSImage object img = AICSImage("http://my-website.com/my_file.tiff") img = AICSImage("s3://my-bucket/my_file.tiff") img = AICSImage("gcs://my-bucket/my_file.tiff") # Or read with specific filesystem creation arguments img = AICSImage("s3://my-bucket/my_file.tiff", fs_kwargs=dict(anon=True)) img = AICSImage("gcs://my-bucket/my_file.tiff", fs_kwargs=dict(anon=True)) # All other normal operations work just fine

Remote reading requires that the file-system specification implementation for the target backend is installed.

• For s3: pip install s3fs
• For gs: pip install gcsfs

See the list of known implementations.

The simpliest method to save your image as an OME-TIFF file with key pieces of metadata is to use the save function.

from aicsimageio import AICSImage AICSImage("my_file.czi").save("my_file.ome.tiff")

Note: By default aicsimageio will generate only a portion of metadata to pass along from the reader to the OME model. This function currently does not do a full metadata translation.

For finer grain customization of the metadata, scenes, or if you want to save an array as an OME-TIFF, the writer class can also be used to customize as needed.

import numpy as np from aicsimageio.writers import OmeTiffWriter image = np.random.rand(10, 3, 1024, 2048) OmeTiffWriter.save(image, "file.ome.tif", dim_order="ZCYX")

See OmeTiffWriter documentation for more details.

In most cases, AICSImage.save is usually a good default but there are other image writers available. For more information, please refer to our writers documentation.

AICSImageIO is benchmarked using asv. You can find the benchmark results for every commit to main starting at the 4.0 release on our benchmarks page.

See our developer resources for information related to developing the code.

If you find aicsimageio useful, please cite this repository as:

Eva Maxfield Brown, Dan Toloudis, Jamie Sherman, Madison Swain-Bowden, Talley Lambert, AICSImageIO Contributors (2021). AICSImageIO: Image Reading, Metadata Conversion, and Image Writing for Microscopy Images in Pure Python [Computer software]. GitHub. https://github.com/AllenCellModeling/aicsimageio

bibtex:

@misc{aicsimageio, author = {Brown, Eva Maxfield and Toloudis, Dan and Sherman, Jamie and Swain-Bowden, Madison and Lambert, Talley and {AICSImageIO Contributors}}, title = {AICSImageIO: Image Reading, Metadata Conversion, and Image Writing for Microscopy Images in Pure Python}, year = {2021}, publisher = {GitHub}, url = {https://github.com/AllenCellModeling/aicsimageio} }

Free software: BSD-3-Clause

(The LIF component is licensed under GPLv3 and is not included in this package) (The Bio-Formats component is licensed under GPLv2 and is not included in this package) (The CZI component is licensed under GPLv3 and is not included in this package)