Validate a tool against IDR data: Load Image with labels from IDR, re-analyze using Cellpose
Version 1

Workflow Type: Jupyter

IDR is based on OMERO and thus all what we show in this notebook can be easily adjusted for use against another OMERO server, e.g. your institutional OMERO server instance.

The main objective of this notebook is to demonstrate how public resources such as the IDR can be used to train your neural network or validate software tools.

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:, 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. StarDist 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 an image (id=6001247) referenced in the paper. The image can be viewed online in the Image Data Resource (IDR).

We will use a predefined model from Cellpose as a starting point. Steps to access data from IDR could be re-used if you wish to create a new model (outside the scope of this notebook).


This notebook uses the environment_cellpose.yml file.

See Setup.

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Version 1 (earliest) Created 1st Jun 2023 at 10:47 by Jean-Marie Burel

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Walczysko, P., & Burel, J.-M. (2023). Validate a tool against IDR data: Load Image with labels from IDR, re-analyze using Cellpose. WorkflowHub.

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Created: 1st Jun 2023 at 10:47

Last updated: 1st Jun 2023 at 11:19

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