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 an interactive GUI with side-by-side result comparison! 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, HOCT, 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
� Interactive Workflow
- Side-by-side table view of original and processed images • Click to instantly compare results • Quickly iterate parameter values • Real-time visual feedback
�📊 Complete Analysis Pipeline
- Segmentation → Tracking → Quantification → Colocalization
🚀 Quick Start
Supports Python 3.11+; commands below use Python 3.12.
# Install napari and the plugin
mamba create -y -n napari-tmidas -c conda-forge python=3.12
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 |
| 🧬 HOCT | Transformer-based cell tracking (Higher-Order Cell Tracking Transformer) | 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 - All 40+ processing functions in one place
- 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
Advanced Features
- Batch Crop Anything - Interactive object cropping with SAM2
- Batch Label Inspection - Manual label verification and editing, with one-click delete/relabel across all timepoints, click-to-split for merged objects, and click-to-merge-neighbors for over-segmented ones
- Multichannel Processing - Channel selection and per-channel processing
💻 Installation
Step 1: Install napari
mamba create -y -n napari-tmidas -c conda-forge python=3.12
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:
Core Scientific Stack:
- NumPy • scikit-image • PyTorch
File Format Support:
- OME-Zarr • tifffile • nd2 • pylibCZIrw • readlif
1 maintainer
Release files for napari-tmidas 0.5.14
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| napari_tmidas-0.5.14.tar.gz | 634.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| napari_tmidas-0.5.14-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.2 MB
Release files / napari_tmidas-0.5.14.tar.gz
| Download URL | napari_tmidas-0.5.14.tar.gz |
|---|---|
| Size | 634.0 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / napari_tmidas-0.5.14-py3-none-any.whl
| Download URL | napari_tmidas-0.5.14-py3-none-any.whl |
|---|---|
| Size | 586.6 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Uploaded via |
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