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https://github.com/aaronwatters/segmentation_viz_workflow
Code and documentation about creating microscopy cell segmentation visualizations
https://github.com/aaronwatters/segmentation_viz_workflow
Last synced: about 1 month ago
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Code and documentation about creating microscopy cell segmentation visualizations
- Host: GitHub
- URL: https://github.com/aaronwatters/segmentation_viz_workflow
- Owner: AaronWatters
- License: apache-2.0
- Created: 2022-04-15T15:06:11.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-11-22T18:25:32.000Z (about 2 years ago)
- Last Synced: 2024-10-14T06:24:46.516Z (2 months ago)
- Language: Python
- Size: 1.42 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# segmentation_viz_workflow
Code and documentation about creating microscopy cell segmentation visualizations.
This package requires python 3.
```
$ python --version
Python 3.6.13 :: Anaconda, Inc.
```
# Installation
The install procedure is not fully automated.
Please install the following dependencies manually in the following order.
```
pip install git+https://github.com/AaronWatters/H5Gizmos
pip install git+https://github.com/AaronWatters/jp_doodle
pip install git+https://github.com/AaronWatters/feedWebGL2
pip install git+https://github.com/flatironinstitute/mouse_embryo_labeller
```
Then clone this repository and in the top level folder of the repository install the module in development mode as follows:
```bash
pip install -e .
```
# Running the examples
The `examples` folder contains an example CSV file with a truncated set of label arrays in a subfolder.
```
examples/segmentation/00008_rescaled_low_cp_masks.tif
examples/segmentation/00000_rescaled_low_cp_masks.tif
examples/segmentation/00007_rescaled_low_cp_masks.tif
examples/segmentation/00002_rescaled_low_cp_masks.tif
examples/segmentation/00005_rescaled_low_cp_masks.tif
examples/segmentation/00004_rescaled_low_cp_masks.tif
examples/segmentation/00003_rescaled_low_cp_masks.tif
examples/segmentation/00006_rescaled_low_cp_masks.tif
examples/segmentation/00001_rescaled_low_cp_masks.tif
examples/segmentation/00009_rescaled_low_cp_masks.tif
examples/stack_2_tree_cell_association.csv
```
## Capturing the surfaces as JSON
The `examples/capture_surfaces.py` shows example code for capturing surface data for cell lineage 1 using the CSV and the arrays.
The script uses a browser interface to extract the geometries.
```bash
% cd examples
% python capture_surfaces.py
```
The processor uses WebGL in a browser to capture the surfaces.
Open the link printed by the script in a browser to establish
the browser connection and start the capture process.
**Important**: The browser tab for capturing the surfaces does not exit automatically.
Close the tab manually once the script completes.
After a successful run the capture script generates the surface data in `examples/surfaces.json`.
## Viewing the surfaces
After the capture process is complete view the surfaces using the `view_surfaces.py` script:
```bash
% python view_surfaces.py
```
Open the link printed by the script in a browser tab to view the surfaces.
Press the load button to load the JSON data (this step is provided for easy javascript debugging).
The viewer should look something like this:
Please use the source code and examples to generate surfaces for other data sets.
# Viewing multiple lineages
The scripts `examples/capture_lineages.py` and `examples/view_lineages.py` illustrate how to
process and view multiple lineages using specified colors for each lineage. Please look to
the scripts for more information.
