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Welcome

steinbock is a framework for multi-channel image processing

The steinbock framework 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 interactively as well as programmatically from within Python scripts.

Overview

At its core, steinbock provides the following functionality:

  • Image preprocessing, including tools 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

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

  • [Pixel classification-based object segmentation] Zanotelli et al. ImcSegmentationPipeline: A pixel classification-based multiplexed image segmentation pipeline. Zenodo, 2017. DOI: 10.5281/zenodo.3841961.
  • [Deep learning-based cell segmentation] Greenwald et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. bioRxiv, 2021. DOI: 10.1101/2021.03.01.431313.

The steinbock framework 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

Contributing

Pull requests are welcome. Please make sure to update documentation as appropriate.

For major changes, please open an issue first to discuss what you would like to change.

To debug steinbock commands using Visual Studio Code and Docker Compose, adapt the command in docker-compose.yml file (e.g. add --version after -m steinbock) and use the "Python: Remote Attach" task (run it twice to prepare the Docker container and start debugging).

Authors

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