cornucopia.contrast

ContrastMixtureTransform

ContrastMixtureTransform(nk=16, keep_background=True, *, shared=False, **kwargs)

Bases: NonFinalTransform

Find intensity modes using a GMM and change their means and covariances.

References

Meyer, M.I., de la Rosa, E., Pedrosa de Barros, N., Paolella, R., Van Leemput, K. and Sima, D.M., 2021. **A contrast augmentation approach to improve multi-scanner generalization in MRI. ** Frontiers in Neuroscience, 15, p.708196.

@article{meyer2021,
  title={A contrast augmentation approach to improve
         multi-scanner generalization in MRI},
  author={Meyer, Maria Ines and de la Rosa, Ezequiel and
          Pedrosa de Barros, Nuno and Paolella, Roberto and
          Van Leemput, Koen and Sima, Diana M},
  journal={Frontiers in Neuroscience},
  volume={15},
  pages={708196},
  year={2021},
  publisher={Frontiers Media SA},
  url={https://www.frontiersin.org/articles/10.3389/fnins.2021.708196}
}

Parameters:

Name	Type	Description	Default
`nk`	`int`	Number of classes	`16`
`keep_background`	`bool`	Do not change background mean/cov. The background class is the class with minimum mean value.	`True`

Other Parameters:

Name	Type	Description
`shared`	`(channels, tensors, channels + tensors, '')`	Apply the same contrast offset to all channels and/or tensors

MixtureFinalTransform

MixtureFinalTransform(z, mu0, sigma0, mu, sigma, **kwargs)

Bases: FinalTransform

Final class that applies the augmentation

ContrastLookupTransform

ContrastLookupTransform(nk=16, keep_background=True, *, shared=False, **kwargs)

Bases: NonFinalTransform

Segment intensities into equidistant bins and change their mean value.

Parameters:

Name	Type	Description	Default
`nk`	`int`	Number of classes	`16`
`keep_background`	`bool`	Do not change background mean/cov. The background class is the class with minimum mean value.	`True`