MercaderLabAnatomy/napari-tmidas: v0.3.1 - Documentation & Installation Clarity Update

📚 What's New

This release focuses on improving user documentation and reducing installation confusion with clearer setup instructions and comprehensive feature guides.

✨ Improvements

Documentation

• ✅ Added comprehensive Batch Crop Anything guide with interactive controls, 2D/3D workflows, and troubleshooting
• ✅ Added comprehensive File Conversion guide covering all supported formats (LIF, ND2, CZI, NDPI, Acquifer) and output options (TIFF, OME-Zarr)
• ✅ Standardized installation instructions across all feature documentation
• ✅ Added detailed performance tips and real-world workflow examples

User Experience

• ✅ Simplified installation: Single clear decision table replacing confusing multiple options
• ✅ Removed redundant instructions: Clarified that all AI packages are included in napari-tmidas[deep-learning]
• ✅ Consistent messaging: Unified installation guidance across Cellpose, CAREamics, Spotiflow, Trackastra, and SAM2 features
• ✅ Updated README: Better organized with 3-step setup and clear feature recommendations

🎯 Key Messaging

Users installing napari-tmidas[deep-learning] now get everything they need:

• Batch Crop Anything (SAM2)
• Cellpose segmentation
• CAREamics denoising
• Spotiflow spot detection
• Trackastra tracking
• VisCy virtual staining

No additional pip installs required!

📖 New Documentation

• Batch Crop Anything - Interactive object segmentation with SAM2
• File Conversion - Multi-format microscopy image conversion

🐛 Bug Fixes & Quality

• Improved NumPy compatibility error handling in intensity_label_filter
• Better error messages for missing dependencies

📦 Installation

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

# Install napari-tmidas with all features
pip install 'napari-tmidas[deep-learning]'