Installation

Requirements

QuPath (Steps 1–3)

• QuPath 0.6.0+
• Cellpose extension installed in QuPath — configure the Cellpose and Cellpose SAM Python paths in Edit → Preferences → Cellpose/Omnipose

• GPU recommended for Cellpose segmentation
• A pre-trained pixel classifier named "Fat in muscle", along with all Groovy scripts and Python scripts, can be downloaded from this GitHub repository

Python (Step 4)

• Python 3.9+
• Conda environment recommended

Download

1. Clone or download

MyoPath/
├── MyoPath1_roi.groovy
├── MyoPath2_segment.groovy
├── MyoPath3_export.groovy
├── MyoPath4_analysis.py
├── project.qpproj                   # QuPath project file
├── classifiers/
│   ├── classes.json
│   └── pixel_classifiers/
│       ├── Fat in muscle.json       # Required pixel classifier
│       └── ...                      # Other optional classifiers
├── data/                            # QuPath slide entries
├── exports/                         # Step 3 output (per-sample)
├── results/                         # Step 4 aggregated output
└── src/
    ├── data_loader.py
    ├── coordinate_processor.py
    ├── tissue_analyzer.py
    ├── visualizer.py
    └── dystrophy_analyzer.py

Pixel Classifiers

The classifiers/pixel_classifiers/ directory is part of the QuPath project and contains pre-trained pixel classifiers in JSON format. The pipeline requires a classifier named Fat in muscle for fat infiltration detection in Step 2. If you are setting up a new QuPath project, you need to train this classifier yourself (see Requirements) or copy it from an existing project.

2. Set up Python environment

conda create -n myopath python=3.10 -y
conda activate myopath

pip install tiatoolbox numpy scipy matplotlib shapely pandas

3. Verify installation

python -c "import tiatoolbox, numpy, scipy, matplotlib, shapely, pandas; print('All dependencies OK')"

Troubleshooting

Cellpose TileFile null error in Step 2

The downsample factor is too small, causing oversized tiles. The default downsample: 10.0 should work. If you still see this error, increase the value (e.g., 12.0 or 15.0).

No muscle fibers detected

• Ensure the ROI covers actual muscle tissue
• Check that Cellpose and its Python environment are properly installed
• Try lowering cellprobThreshold (e.g., from 0 to −2)

Python ModuleNotFoundError

Make sure you run from the MyoPath/ directory, or use --input to specify the exports path.

Conda An unexpected error has occurred after Step 4

An unexpected error has occurred. Conda has prepared the above report.

This is a known conda 25.7.0 bug on Windows with GBK encoding. It does not affect result generation — your output files are produced correctly despite the error message. You can safely ignore it, or suppress it with:

CONDA_NO_PLUGINS=true conda run -n myopath python MyoPath4_analysis.py --all

Out of memory during batch processing

Reduce --cores to limit parallel processes (each sample uses ~2–4 GB RAM).