Metadata-Version: 1.2
Name: lesion_metrics
Version: 0.1.0
Summary: metrics for evaluating lesion segmentations
Home-page: https://github.com/jcreinhold/lesion_metrics
Author: Jacob Reinhold
Author-email: jcreinhold@gmail.com
License: Apache Software License 2.0
Description: ==============
        lesion-metrics
        ==============
        
        
        .. image:: https://img.shields.io/pypi/v/lesion_metrics.svg
                :target: https://pypi.python.org/pypi/lesion-metrics
        
        .. image:: https://img.shields.io/travis/jcreinhold/lesion_metrics.svg
                :target: https://travis-ci.com/jcreinhold/lesion-metrics
        
        .. image:: https://readthedocs.org/projects/lesion-metrics/badge/?version=latest
                :target: https://lesion-metrics.readthedocs.io/en/latest/?version=latest
                :alt: Documentation Status
        
        
        
        
        metrics for evaluating lesion segmentations
        
        
        * 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
        
        Alternatively, you can 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 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.
        
        
        =======
        History
        =======
        
        0.1.0 (2021-05-14)
        ------------------
        
        * First release on PyPI.
        
Keywords: lesion,metrics,segmentation,mri
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
