MOSS - Microscopy Oriented Segmentation with Supervision

A PyQt6-based interactive segmentation tool with U-Net training, prediction, and mask editing capabilities. MOSS provides an intuitive interface for training deep learning models on microscopy images through direct annotation and real-time feedback.

Features

• Interactive Training: Paint masks on images and train U-Net models in real-time
• Multiple Architectures: UNet, UNetDeep, UNetDeepDice, 2.5D models
• Live Predictions: See model predictions as you work
• Refiner Mode: Train a refinement model that learns from your edits
• Batch Processing: Reslice, predict, and vote across multiple views
• Mask Editing Tools: Brush, eraser, fill, and component-based editing
• Multi-User Training: Collaborate with others anywhere (requires relay server)

Installation

Option 1: pip install (recommended)

Mac (Apple Silicon or Intel)

python -m venv moss-env
source moss-env/bin/activate
pip install torch torchvision
pip install -e .

Linux with NVIDIA GPU (CUDA)

python -m venv moss-env
source moss-env/bin/activate
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
pip install -e .

Linux/Windows CPU only

python -m venv moss-env
source moss-env/bin/activate  # On Windows: moss-env\Scripts\activate
pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
pip install -e .

Option 2: conda environment

# Create environment from file
conda env create -f environment.yml

# Activate environment
conda activate moss

# Install the package
pip install -e .

Option 3: Manual installation

# Install dependencies
pip install -r requirements.txt

# Install the package
pip install -e .

Usage

After installation, run:

moss

Or run as a Python module:

python -m segmentation_suite

Keyboard Shortcuts

Key	Action
A / Left Arrow	Previous slice
D / Right Arrow	Next slice
B	Brush tool
E	Eraser tool
H	Hand (pan) tool
F + Click	Fill tool
S	Toggle predictions
Space	Accept hovered prediction component
Shift + Space	Accept ALL predictions (replace mask)
Tab	Capture crop for training (refiner mode)
Ctrl + S	Save project
Ctrl + Z	Undo
+/-	Zoom in/out
Shift + Scroll	Adjust brush size

Project Structure

When you create a project, the following folders are created:

project_folder/
├── project_config.json    # Project settings
├── masks/                 # Saved masks (mask_00000.tif, etc.)
├── train_images/          # Training image crops
├── train_masks/           # Training mask crops
├── checkpoint_*.pth       # Model checkpoints (per architecture)
├── refiner_images/        # Refiner training data
├── refiner_masks_before/  # Mask state before edits
├── refiner_masks_after/   # Mask state after edits
└── refiner_checkpoint.pth # Refiner model checkpoint

Requirements

• Python 3.9+
• PyQt6 6.4+
• PyTorch 2.0+
• CUDA (optional, for GPU acceleration)
• macOS MPS (optional, for Apple Silicon acceleration)

Troubleshooting

PyQt6 issues on Mac

If you get errors about PyQt6, try:

pip install PyQt6 --force-reinstall

PyTorch MPS issues on Mac

For Apple Silicon Macs, ensure you have a recent PyTorch version:

pip install --upgrade torch torchvision

CUDA out of memory

Reduce batch size in the training settings or use a smaller tile size.

Images not loading

Supported formats: TIFF (.tif, .tiff), PNG, JPEG. For best results, use single-channel grayscale TIFF images.

Author

Nelson Medina - Creator and primary developer GitHub: @nelsmedina

Citation

If you use MOSS in your research, please cite:

Medina, N. (2025). MOSS: Microscopy Oriented Segmentation with Supervision [Computer software].
https://github.com/nelsmedina/MOSS

License

MIT License