Cell Tracker

A specialized tool for analyzing T1 transitions in four-cell clusters using microscopy time-lapse data. This package performs automated cell segmentation, tracking, and topological analysis to identify and quantify T1 transitions - critical cell rearrangement events in tissue development.

Features

• Automated Cell Segmentation: Uses Cellpose 4.x for robust cell identification in multi-channel microscopy images
• Cell Tracking: IoU-based tracking maintains consistent cell identities across time frames
• T1 Transition Analysis: Identifies and quantifies T1 transitions by analyzing adjacency relationships
• Topology Analysis: Comprehensive analysis of cell cluster topology and connectivity patterns
• Visualization: Multi-panel visualizations showing segmentation, tracking, and graph analysis
• Export Capabilities: Saves analysis results as CSV files and publication-ready figures

Installation

# Install using pip (recommended)
pip install -e .

# Or using uv (faster)
uv pip install -e .

Quick Start

from cell_tracker.pipeline import analyze_timelapse_data

# Analyze your time-lapse data
results = analyze_timelapse_data(
    data_path='path/to/your/data.npy',
    output_dir='analysis_results',
    start_frame=0
)

Data Format

Input data should be a numpy array with shape (timeframes, channels, height, width):

• Channel 0: Pattern channel (optional)
• Channel 1: Nuclei (used for segmentation)
• Channel 2: Cytoplasm fluorescence (used for segmentation)
• Channel 3: Phase contrast (optional)

Output

The analysis generates:

• Frame-by-frame segmentation visualizations
• T1 transition analysis plots
• CSV files with quantitative data
• Summary statistics and event detection

Example Usage

See test.py for a complete example using the included sample data.

Requirements

• Python ≥ 3.11
• numpy, matplotlib, scikit-image
• cellpose ≥ 4.0
• networkx, scipy, tifffile