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.

Click the image above to watch the demo video on YouTube

Features

• Image I/O: Load .lif, .czi, .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 with fixed-threshold mode for inhibitor experiments
• 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
• Filtered LIF export: Export analyzed scenes once as renamed OME-TIFF/AVI files with an original-to-new-name manifest

Documentation

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

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

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)

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

Troubleshooting Installation

If you encounter errors during installation (especially on Python 3.12):

# Option 1: Upgrade pip and pre-install numpy
pip install --upgrade pip setuptools wheel
pip install numpy
pip install microlive

# Option 2: Use Python 3.10 (most compatible)
conda create -n microlive python=3.10 -y
conda activate microlive
pip install microlive

Common error: ModuleNotFoundError: No module named 'numpy' during pystackreg build

• This occurs when pip tries to build older package versions from source
• Solution: Pre-install NumPy before installing MicroLive

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")
(list_images, list_names, pixel_XY, pixel_Z,
 channel_names, num_channels, *_) = reader.read()

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

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

# Spot detection
spots = mi.SpotDetection(
    image=image[0],  # Single time point [Z, Y, X, C]
    channels_spots=[0],
    channels_cytosol=[],
    channels_nucleus=[],
    masks_complete_cells=masks,
    yx_spot_size_in_px=5,
    threshold_for_spot_detection=100
)

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
│   │   ├── model_downloader.py   # ML model provisioning
│   │   └── resources.py          # Resource paths
│   ├── data/                     # ML models and resources
│   └── pipelines/                # Analysis pipeline modules
├── simulations/                  # Spot simulation & validation (not in pip)
│   ├── spot_simulator.py         # Multi-cell simulation engine
│   ├── run_simulation.py         # CLI runner
│   ├── visualize_results.py      # Visualization tools
│   ├── config_*.yaml             # Configuration examples
│   └── tests/                    # 9-test validation suite (100% pass)
├── 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)
├── pyproject.toml                # Package configuration
└── LICENSE                       # GPL v3 License

License

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

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. Bioinformatics Advances, 2026; vbag095. DOI: 10.1093/bioadv/vbag095

@article{aguilera2026microlive,
  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={Bioinformatics Advances},
  pages={vbag095},
  year={2026},
  doi={10.1093/bioadv/vbag095}
}

Support

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