napari-mAIcrobe 0.0.2

mAIcrobe

mAIcrobe

mAIcrobe: a napari plugin for microbial image analysis.

mAIcrobe is a comprehensive napari plugin that facilitates image analysis workflows of bacterial cells. Combining state-of-the-art segmentation approaches, morphological analysis and adaptable classification models into a napari-plugin, mAIcrobe aims to deliver a user-friendly interface that helps inexperienced users perform image analysis tasks regardless of the bacterial species and microscopy modality.

✨ Why mAIcrobe?

🔬 For Microbiologists

• Automated Cell Segmentation: StarDist2D, Cellpose, and custom U-Net models
• Deep learning classification: 6 pre-trained CNN models for S. aureus cell cycle determination plus support for custom models
• Morphological Analysis: Comprehensive measurements using scikit-image regionprops
• Interactive Filtering: Real-time cell selection based on computed statistics

📊 For Quantitative Research

• Colocalization Analysis: Multi-channel fluorescence quantification
• Automated Reports: HTML reports with visualizations and statistics
• Data Export: CSV export for downstream statistical analysis

🚀 Installation

Standard Installation:

pip install napari-mAIcrobe

Development Installation:

git clone https://github.com/HenriquesLab/mAIcrobe.git
cd mAIcrobe
pip install -e .

🎯 Complete Tutorial →

🏆 Key Features

🎨 Cell Segmentation

• Thresholding: Isodata and Local Average methods with watershed
• StarDist2D: custom models
• Cellpose: cyto3 model
• Custom U-Net Models: custom models

🧠 Single cell Classification

• Pre-trained Models: 6 specialized models for cell cycle determination in S. aureus:
  • DNA+Membrane (Epifluorescence & SIM)
  • DNA-only (Epifluorescence & SIM)
  • Membrane-only (Epifluorescence & SIM)
• Custom Model Support: Load your own TensorFlow models

📊 Comprehensive Morphometry

• Shape Analysis: Area, perimeter, eccentricity
• Intensity Measurements: Fluorescence statistics
• Custom Measurements: Septum detection

📖 Documentation

Guide	Purpose
🚀 Getting Started	Installation to first analysis
🔬 Segmentation Guide	Choose the right segmentation method
📊 Cell Analysis	Complete analysis workflows
🧠 Cell Classification Guide	Cell cycle classification setup
⚙️ API Reference	Programmatic usage

🎯 Analysis Workflow

📄 Single Image Analysis

1. Load Images: Phase contrast and/or fluorescence
2. Segment Cells: Choose segmentation algorithm and parameters
3. Analyze Cells: Extract morphological and intensity features and choose classification model
4. Filter Results: Interactive filtering of cell populations
5. Generate Report: Create comprehensive analysis report

🧪 Sample Data

The plugin includes test datasets for method validation:

• Phase Contrast: S. aureus cells in exponential growth
• Membrane Stain: NileRed fluorescence imaging
• DNA Stain: Hoechst nuclear labeling

Access via napari: File > Open Sample > napari-mAIcrobe

🏃‍♀️ Example Analysis

Input Data:

• Phase contrast image
• Membrane fluorescence
• DNA fluorescence

Analysis Pipeline:

1. Segmentation: Isodata or CellPose's cyto3 identifies individual cells in the phase contrast image
2. Morphology: Calculate morphological and intensity measurements
3. Classification: Cell cycle phase determination using pre-trained CNN model
4. Quality Control: Interactive filtering of analysis results. Select subpopulations based on size, intensity, or classification
5. Report Generation: HTML output

📚 Available Jupyter Notebooks

Explore advanced functionality with included notebooks:

• Cell Cycle Model Training: Train custom classification models
• StarDist Segmentation: Retrain a StarDist segmentation model

🤝 Community

• 🐛 Issues - Report bugs, request features
• 📚 napari hub - Plugin ecosystem

🏗️ Contributing

We welcome contributions! Whether it's:

• 🐛 Bug reports and fixes
• ✨ New segmentation algorithms
• 📖 Documentation improvements
• 🧪 Additional test datasets
• 🤖 New AI models for classification

Quick contributor setup:

git clone https://github.com/HenriquesLab/mAIcrobe.git
cd mAIcrobe
pip install -e .[testing]
pre-commit install

Testing:

# Run tests
pytest -v

# Run tests with coverage
pytest --cov=napari_mAIcrobe

# Run tests across Python versions
tox

📋 Full Contributing Guide →

📜 License

Distributed under the terms of the BSD-3 license, mAIcrobe is free and open source software.

🙏 Acknowledgments

mAIcrobe is developed in the Henriques and Pinho Labs with contributions from the napari and scientific Python communities.

Built with:

• napari - Multi-dimensional image viewer
• TensorFlow - Machine learning framework
• StarDist - Object detection with star-convex shapes
• Cellpose - Generalist cell segmentation
• scikit-image - Image processing library

---

🔬 From the Henriques and Pinho Labs

"Advancing microbiology through AI-powered image analysis."

🚀 Get Started → | 📚 Learn More → | ⚙️ API Docs →