Load ome.zarr Image with labels from public S3 repositories, analyze in parallel using Cellpose and compare results
Version 1

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Learning objectives

  • Read data to analyse from an object store.
  • Analyse data in parallel using Dask.
  • Show how to use public resources to train neural network.
  • Load labels associated to the original data
  • Compare results with ground truth.

The authors of the PLOS Biology paper, "Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures" published in August 2019: https://doi.org/10.1371/journal.pbio.3000388, considered several image segmenation packages, but they did not use the approach described in this notebook.

We will analyse the data using Cellpose and compare the output with the original segmentation produced by the authors. Cellpose was not considered by the authors. Our workflow shows how public repository can be accessed and data inside it used to validate software tools or new algorithms.

We will use a predefined model from Cellpose as a starting point.

Launch

This notebook uses the environment.yml file.

See Setup.

Version History

Version 1 (earliest) Created 1st Jun 2023 at 11:30 by Jean-Marie Burel

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Burel, J.-M., & Walczysko, P. (2023). Load ome.zarr Image with labels from public S3 repositories, analyze in parallel using Cellpose and compare results. WorkflowHub. https://doi.org/10.48546/WORKFLOWHUB.WORKFLOW.495.1
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Created: 1st Jun 2023 at 11:30

Last updated: 1st Jun 2023 at 11:31

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