Implementation of the STAPLE segmentation algorithm
Project description
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.
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 is about 16 times faster for the test images (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). If you need STAPLE with MRF, check out Jorge Cardoso’s NiftySeg.
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
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