Installation

The Python module is distributed on PyPI - The Python Package Index. The package can be installed by running:

python -m pip install sitkibex[zarr]

Wheels from the main branch can be download wheel from Github Actions in the “python-package” artifact.

Dependencies are conventionally specified in pyproject.toml and requirements.txt and therefore installed as dependencies when the wheel is installed. This includes the SimpleITK 2.0 requirement. The optional “zarr” dependency is required for reading OME-NGFF ZARR files, and may be omitted if not needed.

The sitkibex script can also be installed and managed by pipx or uv. For example, to run and automatically install the latest version of sitkibex, you can use:

uvx sitkibex --help

Data

Sample data is available and described on Zenodo:

Any image and transform file format supported by SimpleITK’s IO can be used by sitk-ibex. The NRRD or NGFF image formats, and txt transform file extension are recommended.

Example

The following examples uses CD4 marker channel extracted from the “IBEX4_spleen” data set with ImageJ. The panel 2 is used as the reference coordinates or the “fixed image”. The other panels are registered then resampled to the fixed image. The following uses the sitk-ibex command line interface to perform image registration:

python -m sitkibex registration --affine "spleen_panel2.nrrd@CD4 AF594" "spleen_panel1.nrrd@CD4 AF594" tx_p2_to_p1.txt
python -m sitkibex registration --affine "spleen_panel2.nrrd@CD4 AF594" "spleen_panel3.nrrd@CD4 AF594" tx_p2_to_p3.txt

A quick 2D visualization of the results can be generated with:

python -m sitkibex resample "spleen_panel2.nrrd@CD4 AF594" "spleen_panel1.nrrd@CD4 AF594" tx_p2_to_p1.txt \
       --bin 4 --fusion --projection -o spleen_onto_p2_2d_Panel1.png
python -m sitkibex resample "spleen_panel2.nrrd@CD4 AF594" "spleen_panel3.nrrd@CD4 AF594" tx_p2_to_p3.txt \
       --bin 4 --fusion --projection -o spleen_onto_p2_2d_Panel3.png

The above image fusion renders the fixed image as magenta and the moving as cyan, so when the two are aligned the results are white.

Then apply the registration transform by resampling all channels of the the input images onto panel 2:

python -m sitkibex resample "spleen_panel2.nrrd@CD4 AF594" spleen_panel2.nrrd tx_p2_to_p1.txt \
       -o spleen_onto_p2_panel1.nrrd
python -m sitkibex resample "spleen_panel2.nrrd@CD4 AF594" spleen_panel3.nrrd tx_p2_to_p3.txt \
       -o spleen_onto_p2_panel3.nrrd

Additional Example

Additional sample data:

The sample Imaris files can be converted to OME-NGFF ZARR with bioformats2raw. The ims files contains one series, and for simplicity, the structure is generated without a series index in the hierarchy with the following commands:

bioformats2raw  --series 0 --scale-format-string '%2$d/'  Human_Spleen_Panel1.ims Human_Spleen_Panel1.zarr
bioformats2raw  --series 0 --scale-format-string '%2$d/'  Human_Spleen_Panel2.ims Human_Spleen_Panel2.zarr
bioformats2raw  --series 0 --scale-format-string '%2$d/'  Human_Spleen_Panel2.ims Human_Spleen_Panel3.zarr

These images will be registered based on the common “Hoechst”. The name of the channels are lost in this conversion from Imaris to OME-NGFF ZARR. Some conversions produce “omera” metadata in the ZARR file which contains channel labels, which can be used. When the channel labels are unavailable, the channel index can be used such as the following commands:

python -m sitkibex registration --affine "Human_Spleen_Panel2.zarr@3" "Human_Spleen_Panel1.zarr@2" tx_p2_to_p1.txt
python -m sitkibex registration --affine "Human_Spleen_Panel2.zarr@3" "Human_Spleen_Panel3.zarr@4" tx_p2_to_p3.txt

The quick 2D visualization can be run similarly to the NRRD example. The OME-NGFF ZARR files are not supported for writing, so the resample command can produce NRRD files as well.

How to Cite

If you use the SITK-IBEX package in your work, please cite us:

A. J. Radtke, E. F. Kandov, B. C. Lowekamp, E. Speranza, C. Chu, A. Gola, N. Thakur, R. Shih, L. Yao, Z. R. Yaniv, R. Beuschel, J. Kabat, J. Croteau, J. Davis, J. M. Hernandez, R. N. Germain “IBEX - A versatile multi-plex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues”, Proc Natl Acad Sci, 117(52):33455-33465, 2020, doi:10.1073/pnas.2018488117.

Documentation

The published Sphinx documentation is available here: https://niaid.github.io/sitk-ibex/

The main built Sphinx documentation is available for download from Github Actions under the build as “sphinx-docs”.

Contact

Please use the GitHub Issues for support and code issues related to the sitk-ibex project.