microlive 1.0.4

Live-cell microscopy image analysis and single-molecule measurements

MicroLive

Authors: Luis U. Aguilera, William S. Raymond, Rhiannon M. Sears, Nathan L. Nowling, Brian Munsky, Ning Zhao

About

MicroLive is a Python-based GUI application for live-cell microscopy image analysis and single-molecule measurements. It provides an end-to-end workflow from image loading through particle tracking, colocalization analysis, and statistical analysis.

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Features

• Image I/O: Load .lif, .tif, .ome.tif with metadata extraction and dimension mapping
• Registration: Drift correction via phase correlation
• Segmentation: Cellpose (GPU), watershed, manual ROI, or external mask import
• Photobleaching correction: Exponential decay modeling
• Particle tracking: 2D (TrackPy) and 3D (Big-FISH) detection with multi-channel support
• Automated threshold detection: Hybrid Big-FISH/TrueSpot method
• Trajectory linking: Nearest-neighbor with memory and cluster analysis
• Intensity quantification: Background subtraction, PSF fitting, SNR calculation
• Colocalization: CNN-based, distance-based, and manual verification
• MSD analysis: Per-cell diffusion coefficient calculation
• Correlation: Auto- and cross-correlation with exponential/linear fitting
• Export: PNG, TIFF, CSV, MP4/GIF with full metadata logging

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Documentation

• User Guide — Complete guide to using MicroLive
• Tutorial — Step-by-step tutorials for all workflows
• API Reference — Technical documentation for developers

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Installation

Quick Install (Recommended)

# Create and activate conda environment
conda create -n microlive python=3.10 -y
conda activate microlive

# Install MicroLive
pip install microlive

# Launch the GUI
microlive

That's it! MicroLive will launch with GPU acceleration automatically enabled:

• macOS (Apple Silicon): MPS GPU acceleration works automatically
• Linux/Windows (CPU): Works out of the box

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GPU Acceleration for NVIDIA (Windows/Linux)

For NVIDIA GPU acceleration, install PyTorch with CUDA support before installing MicroLive:

# Create environment
conda create -n microlive python=3.10 -y
conda activate microlive

# Install PyTorch with CUDA 12.4 (adjust version as needed)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124

# Then install MicroLive
pip install microlive

# Launch
microlive

Using Conda Environment Files

Pre-configured environment files are available:

# macOS / CPU
conda env create -f installation/microlive.yml
conda activate microlive

# Windows/Linux with NVIDIA GPU
conda env create -f installation/microlive_cuda.yml
conda activate microlive

Verify GPU Support

from microlive.utils.device import check_gpu_status
check_gpu_status()

Expected output:

PyTorch version: 2.x.x
✅ CUDA available: NVIDIA GeForce RTX ...
   Memory: 8.0 GB

Or for Apple Silicon:

PyTorch version: 2.x.x
✅ MPS available: Apple Silicon GPU (MPS)

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Development Installation

For developers who want to modify the source code:

# Clone the repository
git clone --depth 1 https://github.com/ningzhaoAnschutz/microlive.git
cd microlive

# Create environment
conda create -n microlive python=3.10 -y
conda activate microlive

# Install in editable mode
pip install -e .

# Launch
microlive

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Usage

GUI Application

conda activate microlive
microlive

Programmatic API

import microlive.microscopy as mi

# Load images from a Leica .lif file
reader = mi.ReadLif("experiment.lif")
images = reader.get_images()

# Access image data
image = reader.list_images[0]
print(f"Image shape: {image.shape}")  # (T, Z, Y, X, C)

# Run Cellpose segmentation
cellpose = mi.Cellpose(image_for_segmentation=image[0, 0, :, :, 0])
masks = cellpose.calculate_masks()

# Spot detection
spots = mi.SpotDetection(
    image=image,
    spot_size_yx=5,
    threshold=100
)
detected = spots.detect_spots()

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Project Structure

microlive/
├── microlive/                    # Core package (pip installable)
│   ├── microscopy.py             # Main analysis classes
│   ├── imports.py                # Central import management
│   ├── ml_spot_detection.py      # ML-based spot detection
│   ├── gui/                      # GUI application
│   │   ├── app.py                # Main GUI window
│   │   └── main.py               # CLI entry point
│   ├── utils/                    # Utility modules
│   │   ├── device.py             # GPU detection
│   │   └── resources.py          # Resource paths
│   └── pipelines/                # Analysis pipeline modules
├── docs/                         # Documentation
│   ├── user_guide.md             # User manual
│   ├── tutorial.md               # Step-by-step tutorials
│   └── api_reference.md          # API documentation
├── modeling/                     # Research/development (not in pip package)
├── notebooks/                    # Example Jupyter notebooks
├── installation/                 # Environment files
│   ├── microlive.yml             # Conda env (macOS / CPU)
│   └── microlive_cuda.yml        # Conda env (NVIDIA GPU)
├── tests/                        # Test suite
├── pyproject.toml                # Package configuration
└── LICENSE                       # GPL v3 License

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License

This project is licensed under the GNU General Public License v3 (GPLv3). See LICENSE for details.

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Citation

If you use MicroLive in your research, please cite:

Aguilera LU, Raymond WS, Sears RM, Nowling NL, Munsky B, Zhao N. MicroLive: An Image Processing Toolkit for Quantifying Live-cell Single-Molecule Microscopy. bioRxiv, 2025.
DOI: 10.1101/2025.09.25.678587

@article{aguilera2025microlive,
  title={MicroLive: An Image Processing Toolkit for Quantifying Live-cell Single-Molecule Microscopy},
  author={Aguilera, Luis U and Raymond, William S and Sears, Rhiannon M and Nowling, Nathan L and Munsky, Brian and Zhao, Ning},
  journal={bioRxiv},
  pages={2025--09},
  year={2025},
  publisher={Cold Spring Harbor Laboratory},
  doi={10.1101/2025.09.25.678587}
}

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Support

• Documentation: User Guide | Tutorial | API Reference
• Issues & Contributions: GitHub