ℹ️ Note that we use our ivadomed fork of the MetricsReloaded repository.
# Create and activate a new conda environment
conda create -n metrics_reloaded python=3.10 pip
conda activate metrics_reloaded
# Clone the MetricsReloaded repository (ivadomed fork)
cd ~/code
git clone https://github.com/ivadomed/MetricsReloaded
cd MetricsReloaded
# Install the package
python -m pip install .
# You can alternatively install the package in editable mode:
python -m pip install -e .
The installation instructions are available also here.
You can use the compute_metrics_reloaded.py wrapper script to compute metrics using the MetricsReloaded package.
Example usage:
python compute_metrics_reloaded.py
-reference sub-001_T2w_seg.nii.gz
-prediction sub-001_T2w_prediction.nii.gz
The metrics to be computed can be specified using the -metrics argument. For example, to compute only the Dice
similarity coefficient (DSC) and Normalized surface distance (NSD), use:
python compute_metrics_reloaded.py
-reference sub-001_T2w_seg.nii.gz
-prediction sub-001_T2w_prediction.nii.gz
-metrics dsc nsd
ℹ️ See https://arxiv.org/abs/2206.01653v5 for nice figures explaining the metrics!
The output is saved to a CSV file, for example:
reference prediction label dsc nsd EmptyRef EmptyPred
seg.nii.gz pred.nii.gz 1.0 0.819 0.945 False False
seg.nii.gz pred.nii.gz 2.0 0.743 0.923 False False
ℹ️ The script is compatible with both binary (voxels with label values 1) and multi-class segmentations (voxels with
label values 1, 2, etc.; e.g., nnunet region-based).
ℹ️ The metrics are computed for each unique label (class) in the reference (ground truth) image.
- MetricsReloaded documentation
- MetricsReloaded publication
- MetricsReloaded preprint - preprint contains more figures than the publication