Skip to content

Logo

Welcome

steinbock is a toolkit for processing multiplexed tissue images

The steinbock toolkit comprises the following components:

  • The steinbock Python package with the integrated steinbock command-line interface (CLI)
  • The steinbock Docker container interactively exposing the steinbock command-line interface, with supported third-party software (e.g. Ilastik, CellProfiler) pre-installed

Modes of usage

steinbock can be used both interactively using the command-line interface (CLI) and programmatically from within Python scripts.

Overview

At its core, steinbock provides the following functionality:

  • Image preprocessing, including utilities for tiling/stitching images
  • Pixel classification, to enable pixel classification-based image segmentation
  • Image segmentation, to identify objects (e.g. cells or other regions of interest)
  • Object measurement, to extract single-cell data, cell neighbors, etc.
  • Data export, to facilitate downstream data analysis
  • Visualization of multiplexed tissue images

Downstream single-cell data analysis

steinbock is a toolkit for extracting single-cell data from multiplexed tissue images and NOT for downstream single-cell data analysis.

While all steinbock functionality can be used in a modular fashion, the toolkit was designed for - and explicitly supports - the following image segmentation workflows:

  • [Ilastik/CellProfiler] Zanotelli et al. ImcSegmentationPipeline: A pixel classification-based multiplexed image segmentation pipeline. Zenodo, 2017. DOI: 10.5281/zenodo.3841961.
  • [DeepCell/Mesmer] Greenwald et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nature Biotechnology, 2021. DOI: 10.1038/s41587-021-01094-0.
  • [Cellpose] Stringer et al. Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 2021. DOI: 10.1038/s41592-020-01018-x

The steinbock toolkit is extensible and support for further workflows may be added in the future. If you are missing support for a workflow, please consider filing an issue on GitHub.

Resources

Code: https://github.com/BodenmillerGroup/steinbock

Documentation: https://bodenmillergroup.github.io/steinbock

Issue tracker: https://github.com/BodenmillerGroup/steinbock/issues

Discussions: https://github.com/BodenmillerGroup/steinbock/discussions

Workshop 2023: https://github.com/BodenmillerGroup/ImagingWorkshop2023

Citing steinbock

Please cite the following paper when using steinbock in your work:

Quote

Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). https://doi.org/10.1038/s41596-023-00881-0

@article{Windhager2023,
  author = {Windhager, Jonas and Zanotelli, Vito R.T. and Schulz, Daniel and Meyer, Lasse and Daniel, Michelle and Bodenmiller, Bernd and Eling, Nils},
  title = {An end-to-end workflow for multiplexed image processing and analysis},
  year = {2023},
  doi = {10.1038/s41596-023-00881-0},
  URL = {https://www.nature.com/articles/s41596-023-00881-0},
  journal = {Nature Protocols}
}

If you have issues accessing the manuscript, please reach out to us and we can share the PDF version.