Neurite_Connect

A Python script for identifying nuclei and connections from stained images.

Description

This project includes a Python script that processes two TIFF images: one for nuclei and one for cell walls. The script performs image segmentation, identifies nuclei, computes connections between nuclei, and generates various output files including metrics and connection graphs.

Files

• process_images.py: The main script that processes the images and generates the outputs.
• requirements.txt: A file listing all the dependencies required to run the script.

Setup

Prerequisites

• Python 3.x
• pip (Python package installer)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/Neuro_Connect.git
   cd Neuro_Connect
   

2. Create and activate a virtual environment (optional but recommended):

   python -m venv env
   source env/bin/activate    # On Windows, use `env\\Scripts\\activate`
   

3. Install the dependencies:

   pip install -r requirements.txt
   

Usage

Run the script with the paths to your two TIFF images as arguments:

python process_images.py path/to/nuclei_image.tif path/to/cell_walls_image.tif

Example

python process_images.py data/nuclei_image.tif data/cell_walls_image.tif

Output

The script generates the following output files named based on the first image provided:

1. Final Combined Image: nuclei_image_final_combined.png
2. Centroids CSV: nuclei_image_centroids.csv
3. Metrics TXT: nuclei_image_metrics.txt
4. Nuclei Connection Graph: cell_walls_image_nuclei_connection_graph_100px.png
5. Connection Metrics CSV: cell_walls_image_connection_metrics.csv
6. Edges CSV: cell_walls_image_edges.csv
7. connection metrics: connection_metrics.csv

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

This project is licensed under the MIT License - see the LICENSE file for details.