napari-tmidas 0.5.2

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

napari-tmidas

Need fast batch processing for confocal & whole-slide microscopy images of biological cells and tissues?

This open-source napari plugin integrates state-of-the-art AI + analysis tools in one GUI! Transform, analyze, and quantify microscopy data at scale including deep learning - from file conversion to segmentation, tracking, and analysis.

✨ Key Features

🤖 AI Methods Built-In

• Virtual staining (VisCy) • Denoising (CAREamics) • Spot detection (Spotiflow) • Segmentation (Cellpose, Convpaint) • Tracking (Trackastra, Ultrack)
• 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 (SAM2, Cellpose, Spotiflow, etc.) auto-install into isolated environments 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
🎨 Convpaint	Custom semantic/instance segmentation	Guide
📈 Trackastra	Transformer-based cell tracking	Guide
🔗 Ultrack	Cell tracking based on segmentation ensemble	Guide

Core Workflows

• File Conversion - Multi-format microscopy file conversion (LIF, ND2, CZI, NDPI, Acquifer)
• Batch Processing - Label operations, filters, channel splitting
• Frame Removal - Interactive human-in-the-loop frame removal from time series
• Label-Based Cropping - Interactive ROI extraction with label expansion
• 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
Standard installation	pip install napari-tmidas
Want the latest dev features	pip install git+https://github.com/MercaderLabAnatomy/napari-tmidas.git

🖼️ 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.

📋 TODO

Memory-Efficient Zarr Streaming

Current Limitation: Processing functions pre-allocate full output arrays in memory before writing to zarr. For large TZYX time series (e.g., 100 timepoints × 1024×1024×20), this requires ~8+ GB peak memory even when using zarr output.

Planned Enhancement: Implement incremental zarr writing across all processing functions:

• Process one timepoint at a time
• Write directly to zarr array on disk
• Keep only single timepoint in memory (~80 MB vs 8 GB)
• Maintain OME-Zarr metadata and chunking

Impact: Enable processing of arbitrarily large time series limited only by disk space, not RAM. Critical for high-throughput microscopy workflows.

Affected Functions: Convpaint prediction, Cellpose segmentation, CAREamics denoising, VisCy virtual staining, Trackastra tracking, and all batch processing operations with zarr output.

🤝 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:

AI/ML Methods:

• Cellpose • Convpaint • VisCy • CAREamics • Spotiflow • Trackastra • Ultrack • SAM2

Core Scientific Stack:

• NumPy • scikit-image • PyTorch

File Format Support:

• OME-Zarr • tifffile • nd2 • pylibCZIrw • readlif

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