jcreinhold/lesion-metrics

metrics for evaluating lesion segmentations

lesion-metrics

Various metrics for evaluating lesion segmentations [1]

• Free software: Apache Software License 2.0
• Documentation: https://lesion-metrics.readthedocs.io.

Install

The easiest way to install the package is with:

pip install lesion-metrics

To install the dependencies of the CLI, install with:

pip install "lesion-metrics[cli]"

You can also download the source and run:

python setup.py install

Basic Usage

You can generate a report of lesion metrics for a directory of predicted labels and truth labels with the CLI:

lesion-metrics -p predictions/ -t truth/ -o output.csv

Or you can import the metrics and run them on label images:

import nibabel as nib
from lesion_metrics.metrics import dice
pred = nib.load('pred_label.nii.gz').get_fdata()
truth = nib.load('truth_label.nii.gz').get_fdata()
dice_score = dice(pred, truth)

References

[1] Carass, Aaron, et al. "Longitudinal multiple sclerosis lesion segmentation: resource and challenge." NeuroImage 148 (2017): 77-102.

lesion-metrics's Issues

Add pre-commit hooks

Add pre-commit hooks to format code, remove trailing whitespce, etc.

Add numpy mypy plugin

🚀 Feature

Add numpy mypy plugin to mypy.ini

Update pymedio imports

v0.2.0 of pymedio changed the name of the import to pymedio from medio; update this package.

Improve contributing information

📚 Documentation

The contributing information is out-of-date and generally lacking. It needs to be updated so it's easier to contribute to this project.

Add citation file

📚 Documentation

Citation file

Add lesion count to metrics

🚀 Feature

Add number of lesions as a column in the lesion-metrics report

Motivation

Lesion count is an important metric to understand the global structure of lesions in the segmentation mask.