Metadata-Version: 1.1
Name: staple
Version: 0.3.2
Summary: Implementation of the STAPLE segmentation algorithm
Home-page: https://github.com/fepegar/staple
Author: Fernando Perez-Garcia
Author-email: fernando.perezgarcia.17@ucl.ac.uk
License: MIT license
Description: ======
        STAPLE
        ======
        
        
        .. image:: https://img.shields.io/badge/License-MIT-yellow.svg
                :target: https://opensource.org/licenses/MIT
                :alt: License
        
        .. image:: https://img.shields.io/pypi/v/staple.svg
                :target: https://pypi.python.org/pypi/staple
                :alt: PyPI
        
        .. image:: https://img.shields.io/travis/fepegar/staple.svg
                :target: https://travis-ci.org/fepegar/staple
                :alt: CI
        
        .. image:: https://readthedocs.org/projects/staple/badge/?version=latest
                :target: https://staple.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        .. image:: https://coveralls.io/repos/github/fepegar/staple/badge.svg?branch=master
                :target: https://coveralls.io/github/fepegar/staple?branch=master
                :alt: Test coverage
        
        .. image:: https://pyup.io/repos/github/fepegar/staple/shield.svg
             :target: https://pyup.io/repos/github/fepegar/staple/
             :alt: Updates
        
        
        
        Python implementation of the Simultaneous Truth and Performance Level
        Estimation (STAPLE) algorithm for generating ground truth volumes from
        a set of binary segmentations.
        
        The STAPLE algorithm is described in
        `S. Warfield, K. Zou, W. Wells, Validation of image segmentation and
        expert quality with an expectation-maximization algorithm in MICCAI 2002:
        Fifth International Conference on Medical Image Computing and
        Computer-Assisted Intervention, Springer-Verlag, Heidelberg, Germany, 2002,
        pp. 298-306 <https://www.ncbi.nlm.nih.gov/pubmed/15250643/>`_.
        
        
        Installation
        ------------
        
        ::
        
           $ pip install staple
        
        
        Usage
        -----
        
        ::
        
        $ staple seg_1.nii.gz seg_2.nii.gz seg_3.nii.gz result.nii.gz
        
        
        Caveats
        -------
        
        - The `SimpleITK implementation <https://itk.org/SimpleITKDoxygen/html/classitk_1_1simple_1_1STAPLEImageFilter.html>`_
          is about 16 times faster for the
          `test images <https://github.com/fepegar/staple/blob/master/tests/itk_urls.txt>`_
          (0.7 s vs 11.8 s).
          The implementation in this repository is mostly for educational purposes.
        - Markov random field (MRF) postprocessing is not implemented (nor is it in the
          `ITK version <https://github.com/InsightSoftwareConsortium/ITK/blob/master/Modules/Filtering/ImageCompare/include/itkSTAPLEImageFilter.hxx>`_).
          If you need STAPLE with MRF, check out Jorge Cardoso's
          `NiftySeg <https://github.com/KCL-BMEIS/NiftySeg/blob/master/seg-lib/_seg_LabFusion.cpp#L648-L650>`_.
        
        
        Credits
        -------
        
        This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        
Keywords: staple
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.5
Classifier: Operating System :: OS Independent
