napari-tmidas 0.3.5

A plugin for batch processing of confocal and whole-slide microscopy images of biological tissues

napari-tmidas

AI-powered batch processing for microscopy images

Transform, analyze, and quantify microscopy data at scale with deep learning - from file conversion to segmentation, tracking, and analysis.

✨ Key Features

🤖 5 AI Methods Built-In

• Virtual staining (VisCy) • Denoising (CAREamics) • Spot detection (Spotiflow) • Segmentation (Cellpose) • Tracking (Trackastra)
• Auto-install in isolated environments • No dependency conflicts • GPU acceleration

🔄 Universal File Conversion

• Convert LIF, ND2, CZI, NDPI, Acquifer → TIFF or OME-Zarr
• Preserve spatial metadata automatically

⚡ Batch Processing

• Process entire folders with one click • 40+ processing functions • Progress tracking & quality control

📊 Complete Analysis Pipeline

• Segmentation → Tracking → Quantification → Colocalization

🚀 Quick Start

# Install napari and the plugin
mamba create -y -n napari-tmidas -c conda-forge python=3.11
mamba activate napari-tmidas
pip install "napari[all]"
pip install napari-tmidas

# Launch napari
napari

Then find napari-tmidas in the Plugins menu. Watch video tutorials →

💡 Tip: AI methods auto-install their dependencies on first use - no manual setup required!

📖 Documentation

AI-Powered Methods

Method	Description	Documentation
🎨 VisCy	Virtual staining from phase/DIC	Guide
🔧 CAREamics	Noise2Void/CARE denoising	Guide
🎯 Spotiflow	Spot/puncta detection	Guide
🔬 Cellpose	Cell/nucleus segmentation	Guide
📈 Trackastra	Cell tracking over time	Guide

Core Workflows

• File Conversion - Multi-format microscopy file conversion (LIF, ND2, CZI, NDPI, Acquifer)
• Batch Processing - Label operations, filters, channel splitting
• Quality Control - Visual QC with grid overlay
• Quantification - Extract measurements from labels
• Colocalization - Multi-channel ROI analysis

Advanced Features

• Batch Crop Anything - Interactive object cropping with SAM2
• Batch Label Inspection - Manual label verification and editing
• SciPy Filters - Gaussian, median, morphological operations
• Scikit-Image Filters - CLAHE, thresholding, edge detection

💻 Installation

Step 1: Install napari

mamba create -y -n napari-tmidas -c conda-forge python=3.11
mamba activate napari-tmidas
python -m pip install "napari[all]"

Step 2: Install napari-tmidas

Your Needs	Command
Just process & convert images	pip install napari-tmidas
Need AI features (SAM2, Cellpose, Spotiflow, etc.)	pip install 'napari-tmidas[deep-learning]'
Want the latest dev features	pip install git+https://github.com/MercaderLabAnatomy/napari-tmidas.git

Recommended for most users: pip install 'napari-tmidas[deep-learning]'

🖼️ Screenshots

File Conversion Widget

Convert proprietary formats to open standards with metadata preservation.

Batch Processing Interface

Select files → Choose processing function → Run on entire dataset.

Label Inspection

Inspect and manually correct segmentation results.

SAM2 Crop Anything

Interactive object selection and cropping with SAM2.

🤝 Contributing

Contributions are welcome! Please ensure tests pass before submitting PRs:

pip install tox
tox

📄 License

BSD-3 License - see LICENSE for details.

🐛 Issues

Found a bug or have a feature request? Open an issue

🙏 Acknowledgments

Built with napari and powered by:

• Cellpose • VisCy • CAREamics • Spotiflow • Trackastra • SAM2

---