This Python package was implemented as part of the development of the Iterative Bleaching Extends Multiplexity (IBEX) imaging technique. It enables the alignment of multiple cycles of fluorescence images, acquired using IBEX. A repeated marker is used to register all panels to a selected panel (in the registration nomenclature this is the fixed image). After registration all panels are resampled onto the fixed image.
While this method was developed for a specific imaging protocol, it will likely work for other sequential protocols that contain a repeated marker. The registration approach is implemented using the SimpleITK toolkit registration framework.
The key implementation aspects include:
- Multi-phase based approach with robust initialization.
- Multi-resolution and point sampling.
- Affine transformation model.
- Use of correlation as optimized similarity metric.
The Python module is distributed as a wheel binary package. To install the latest tagged release from the Github Releases page with pip, run:
python -m pip install sitkibex --find-links https://github.com/niaid/sitk-ibex/releases
Wheels from the master branch can be download wheel from Github Actions in the "python-package" artifact.
Dependencies are conventionally specified in setup.py and requirements.txt and therefore installed as dependencies when the wheel is installed. This includes the SimpleITK 2.0 requirement.
Sample data is available and described on Zenodo:
Any image and transform file format supported by SimpleITK's IO can be use by sitk-ibex. The 3D nrrd format, and txt transform file extension are recommended.
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
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.
The published Sphinx documentation is available here: https://niaid.github.io/sitk-ibex/
The master built Sphinx documentation is available for download from Github Actions under the build as "sphinx-docs".
Please use the GitHub Issues for support and code issues related to the sitk-ibex project.
Additionally, we can be emailed at: bioinformatics@niaid.nih.gov Please include "sitk-ibex" in the subject line.