MetricsReloaded_quick_start_guide.md

MetricsReloaded quick-start guide

Useful links:

• MetricsReloaded GitHub page
• MetricsReloaded documentation
• MetricsReloaded publication

Installation

Official installation instructions are available here.

Note Always install MetricsReloaded inside a virtual environment.

# Create and activate a new conda environment
conda create -n metrics_reloaded python=3.10 pip
conda activate metrics_reloaded

# Clone the repository
cd ~/code
git clone https://github.com/csudre/MetricsReloaded.git
cd MetricsReloaded

# Install the package
python -m pip install .
# You can alternatively install the package in editable mode:
python -m pip install -e .

Usage

You can use the compute_metrics_reloaded.py script to compute metrics using the MetricsReloaded package.

python compute_metrics_reloaded.py -reference sub-001_T2w_seg.nii.gz -prediction sub-001_T2w_prediction.nii.gz 

Default metrics (semantic segmentation): - Dice similarity coefficient (DSC) - Normalized surface distance (NSD) (for details, see Fig. 2, Fig. 11, and Fig. 12 in https://arxiv.org/abs/2206.01653v5)

The script is compatible with both binary and multi-class segmentation tasks (e.g., nnunet region-based).

The metrics are computed for each unique label (class) in the reference (ground truth) image.

The output is saved to a JSON file, for example:

{
    "reference": "sub-001_T2w_seg.nii.gz",
    "prediction": "sub-001_T2w_prediction.nii.gz",
    "1.0": {
        "dsc": 0.8195991091314031,
        "nsd": 0.9455782312925171
    },
    "2.0": {
        "dsc": 0.8042553191489362,
        "nsd": 0.9580573951434879
    }

}