~\anaconda3\envs\imctools\lib\site-packages\imctools\scripts\exportacquisitioncsv.py in export_acquisition_csv(fol_ome, fol_out, outname)ģ7 dat_meta.to_csv(os.path.join(fol_out, outname+SUF_CSV), index=False) > 1 exportacquisitioncsv.export_acquisition_csv(folder_ome, fol_out=folder_cp) ValueError Traceback (most recent call last) The csv file can be found in the data/cpout folderĮxportacquisitioncsv.export_acquisition_csv(folder_ome, fol_out=folder_cp) Generate a csv with all the acquisition metadata I would really appreciate the help! Thank you very much!īelow I have attached the respective screenshots. mcd-files and the panel-.csv-file multiple times and also the directories but I just cannot resolve the issue. I am getting a 'No objects to concatenate' error when trying to use the pipeline. If you use this workflow for your research, please cite us: To contribute to this work, please fork the repository, make changes to it and open a pull request. In return we would like you to be considerate and give us and others feedback if you find a bug/issue and raise a GitHub Issue on the affected projects or on this page. We freely share this pipeline in the hope that it will be useful for others to perform high quality image segmentation and serve as a basis to develop more complicated open source IMC image processing workflows. Changelogįor changes in specific releases, please refer to the CHANGELOG. The slides briefly explain why we chose this approach to image segmentation and provide help to run the pipeline. This pipeline was presented at the 2019 Imaging Mass Cytometry User Group Meeting. Documentationįor a more detailed overview on the individual analysis steps, please visit. To test these pipelines on example data, please run the scripts/download_examples.ipynb script.
Install cellprofiler conda install#
Usageįor the main part of the analysis, you will need to install Ilastik and CellProfiler.īefore being able to pre-process the data, you will need to setup the environment:
The concepts applied here to IMC data can also be transfered to data generated by other highly multiplexed imaging modalities.įor a general overview on IMC as technology and data processing tasks, please refer to /IMCWorkflow. This pipeline was developed in the Bodenmiller laboratory at the University of Zurich ( to segment hundreds of highly multiplexed imaging mass cytometry (IMC) images. This repository showcases the basis of the workflow with step-by-step instructions.Īs an alternative and dockerized version of the pipeline, check out steinbock. It is streamlined by using the imcsegpipe python package available via this repository as well as custom CellProfiler modules ( ImcPluginsCP, release v4.2.1).
The pipeline is based on CellProfiler (tested v4.2.1) for segmentation and Ilastik (tested v1.3.3post3) for pixel classification. A flexible multiplexed image segmentation pipeline based on pixel classification Introduction
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