README - Calcium Correlation Tool 🔬

This repository provides a pipeline for cell segmentation, tracking, and correlation analysis in time-lapse microscopy data. Utilizing deep learning models like Cellpose, it enables precise segmentation and tracking of cells across frames, offering insights into their morphological changes and fluorescence intensity variations over time. The workflow includes:

• Automated segmentation using deep learning.
• Interactive refinement via Napari.
• Tracking cell movement across frames.
• Correlation analysis to uncover dynamic cellular behaviors.

🏗 Repository Overview

📂 File	📌 Description
0_segmentation_utils.py	Core segmentation & tracking functions (Cellpose)
1_generate_masks.ipynb	Generates segmentation masks from .tif images
2_tracking_refinement.ipynb	Filters, refines, and visualizes tracked cells
3_correlation_analysis.ipynb	Performs correlation analysis on tracked cell properties

Detailed file descriptions

0_segmentation_utils.py

• Contains helper functions for cell segmentation.
• Uses Cellpose for deep-learning-based segmentation.
• Provides functions for:
  • Image preprocessing before segmentation.
  • Running segmentation models.
  • Post-processing segmented masks (e.g., filtering, smoothing).
  • Visualizing segmentation results.

1_generate_masks.ipynb

• Implements the segmentation pipeline for generating cell masks.
• Steps include:
  • Loading time-lapse microscopy data.
  • Running segmentation using Cellpose.
  • Saving segmented masks for further analysis.
  • Interactive refinement using Napari for manual corrections.

2_tracking_refinement.ipynb

• Tracks cell movements across time-lapse frames.
• Key functionalities:
  • Assigns unique IDs to segmented cells in consecutive frames.
  • Corrects tracking errors using heuristic rules.
  • Provides visualization tools for overlaying tracked paths.
  • Allows interactive refinement of cell tracks in Napari.

3_correlation_analysis.ipynb

• Analyzes dynamic cell behaviors through correlation studies.
• Includes:
  • Extracting fluorescence intensity over time for each cell.
  • Computing correlations between intensity signals of different cells.
  • Filtering significant correlations for network analysis.
  • Visualizing correlation networks with Napari.

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🛠️ Installation

🏡 Environment Setup

Run the following code in Anaconda PowerShell Prompt to create the conda environment:

conda create -n cctenv python=3.9 numpy scipy pandas matplotlib tqdm scikit-image tifffile napari cellpose opencv pillow networkx

After installing the environment run the following code to activate it:

conda activate cctenv

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🖥️ Segmentation Setup

🗂️ Workflow

It is recommended to download Workflow.zip and extract it somewhere on your workstation for better directory structure.

Click to expand

User                                    # YourName
  │
  ├── masks_tracked                     # folders with masked .tif files are stored here
  │   ├── Model3                        # an instance of such a folder
  │   └── YourNewModel                  # your instance of such a folder
  │
  ├── pkl_data                          # folders with .pkl files are stored here
  │   ├── Model3                        # an instance of such a folder
  │   └── YourNewModel                  # your instance of such a folder
  │
  ├── plots                             # plots are saved here
  │   └── latex                         # tex files for latex tables are saved here
  │
  ├── raw_data                          # folders with full framed raw .tif files
  │   ├── Model3                        # an instance of such a folder
  │   └── YourNewModel                  # your instance of such a folder
  │
  ├── saved_models                      # folders with cellpose models are stored here
  │   ├── Model3                        # an instance of such a folder
  │   └── YourNewModel                  # your instance of such a folder
  │
  └── training_images_for_cellpose      # used for cellpose training
      ├── cellpose_train                # store files from label and raw here
      │   └── models                    # cellpose model gets saved here
      │
      ├── label                         # labeled raw single frame .tif files
      └── raw                           # raw single frame .tif files

🩻 Image Conversion

Use ImageJ to convert your .czi images to .tif files and save them in ...\User\raw_data. Make sure you save single-framed images from your .tif files in in ...\User\training_images_for_cellpose\raw.

🪄 Manual Segmentation

Before training, create labels using Napari (make sure you have the conda environment activated):

napari

Once open, drag raw images into Napari and manually create labels.

📐 Creating a Model

Ensure the single-framed images (both raw and labeled) are in ...\User\training_images_for_cellpose\, then run:

python -m cellpose --train --use_gpu --verbose --n_epochs 2000 --dir ...\User\training_images_for_cellpose\cellpose_train\ --img_filter _ --mask_filter _label --pretrained_model None

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📚 Running the Notebooks

To start VSCode, make sure you have the conda environment activated so that you can run the following code in the Anaconda PowerShell Prompt:

code

Step-by-step instructions

📕 Generate Segmentation Masks

Execute 1_generate_masks.ipynb to segment cells in your microscopy data:

• Open the notebook in VSCode.
• Load raw microscopy images from the specified directory.
• Define segmentation parameters (occurrence_limit, dist_limit).
• Use Cellpose to segment cells.
• Run the segmentation pipeline and generate cell masks.
• Save the segmented masks for further analysis.

📗 Analyze & Refine Masks

Execute 2_tracking_refinement.ipynb to track and refine cell segmentation:

• Load segmented masks from the previous step.
• Use a tracking algorithm to assign unique IDs to cells across frames.
• Apply filtering (get_common_cells) to remove unreliable labels.
• Use Napari for manual correction of incorrectly placed labels.
• Ensure temporal consistency of cell masks.
• Save the refined segmentation and tracking results.

📘 Perform Correlation Analysis

Execute 3_correlation_analysis.ipynb to analyze fluorescence intensity correlations:

• Load .pkl files containing tracked cell data.
• Load raw microscopy images and corresponding segmented masks.
• Compute fluorescence intensity variations over time for each cell.
• Perform correlation analysis between different cells.
• Visualize and filter significant correlations using Napari.
• Save correlation plots and tables for further analysis.

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🧰 Usage

This pipeline is designed for time-lapse microscopy image analysis, specifically:

• Tracking cell movement and morphological changes.
• Measuring fluorescence intensity variations over time.
• Detecting correlations between cellular behaviors.

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🤝 Contributing

We welcome contributions! Here's how you can help:

🐞 Reporting Issues

• If you find a bug, please open an issue here.
• Provide steps to reproduce the issue, expected behavior, and actual behavior.

🪛 Improving the Code

• Fork the repository.
• Create a feature branch: git checkout -b feature-name
• Commit your changes: git commit -m "Added new feature"
• Push your branch: git push origin feature-name
• Open a pull request

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