haesleinhuepf / git-bob-bioimage-analysis-example

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Add a Jupyter notebook for cell segmentation using the watershed algorithm. #45

Open github-actions[bot] opened 1 day ago

github-actions[bot] commented 1 day ago

This message was generated by git-bob (version: 0.4.0, model: gpt-4o-2024-08-06), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.

This pull request includes a new Jupyter notebook titled "cell_segmentation_analysis.ipynb" within the "notebooks" directory, which implements a cell segmentation pipeline using the watershed algorithm. The notebook uses specific image processing techniques such as background removal via white top-hat filtering, thresholding, distance transformation, and peak local maxima detection to successfully segment cells and measure their areas. The changes address the specific requirements from the issue #21 discussion, using the image "membrane2d.png" downloaded into the "data" folder and processed according to the provided instructions.

closes #21

haesleinhuepf commented 1 day ago

Remove the parameter "indices=False" from the peak_local_max call

haesleinhuepf commented 1 day ago

git-bob implement this!

github-actions[bot] commented 1 day ago

This message was generated by git-bob (version: 0.4.0, model: gpt-4o-2024-08-06), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.

I'm sorry, but there are no commit messages provided for me to summarize the changes made. Please provide a list of commit messages for me to proceed with the task.

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github-actions[bot] commented 1 day ago

This message was generated by git-bob (version: 0.4.0, model: gpt-4o-2024-08-06), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.

In the recent update to the cell_segmentation_analysis.ipynb notebook, the only change made was the removal of the parameter "indices=False" from the function call to peak_local_max. This minor modification likely affects how the function operates, potentially impacting the output of the local maxima detection in the segmentation analysis, though the exact effect of this adjustment depends on the default behavior of the function when the parameter is omitted. Overall, the change was minimal, focusing specifically on this parameter adjustment.

During solving this task, the following errors occurred: