deep_particle_tracking

A Python application for deep learning-based particle tracking in microscopy images.

Overview

Languages: Python

Documentation

Deep Particle Tracker

A Python application for deep learning-based particle tracking in microscopy images.

Overview

Deep Particle Tracker is a comprehensive tool for detecting and tracking fluorescent particles in microscopy image sequences using deep learning. The application integrates:

Simulation of realistic microscopy data
Neural network training for particle detection
Tracking of particles across frames
Visualization of results

The application leverages the power of convolutional neural networks, particularly U-Net architectures with ConvLSTM layers, to process multiple frames simultaneously for improved detection and tracking.

Features

Data Simulation: Generate realistic particle data with various motion models, PSF types, and noise characteristics
Model Training: Train deep learning models on simulated or experimental data
Prediction: Apply trained models to detect particles and track them across frames
Visualization: Interactive visualization of results with tracks and probability maps
User-Friendly GUI: Intuitive interface for all operations

Installation

Requirements

Python 3.7 or newer
PyTorch 1.7 or newer
PyQt5 for the GUI
Various scientific Python packages (numpy, scipy, matplotlib, etc.)

Installation Steps

Clone the repository:

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

Install dependencies:
```
pip install -r requirements.txt
```
Install the package:
```
pip install -e .
```

Usage

Starting the Application

Run the following command to start the application:

python main.py

Or if installed via pip:

deep_particle_tracker

Command Line Options

--debug: Enable debug logging
--cpu: Force CPU mode (disable GPU)

Using the GUI

The application has three main tabs:

Simulation: Create simulated particle data
Training: Train model

[View full README on GitHub]