mimosa_data: MIMoSA Training Data Frame
In neuroconductor/mimosa: 'MIMoSA': A Method for Inter-Modal Segmentation Analysis

Description Usage Arguments Value

This function creates the training vectors from a single MRI study that has FLAIR, T1, T2, and PD volumes as well as binary masks of lesions. The function can create a tissue mask for the data (or the user can supply a brain mask), the candidate voxels for lesion segmentation, smoothed volumes, and coupling maps. The user may supply already normalized data if they wish to use an alternative normalization method.

mimosa_data(
  brain_mask,
  FLAIR,
  T1,
  T2 = NULL,
  PD = NULL,
  tissue = FALSE,
  gold_standard = NULL,
  normalize = "no",
  cand_mask = NULL,
  slices = NULL,
  orientation = c("axial", "coronal", "sagittal"),
  cores = 1,
  verbose = TRUE
)

`brain_mask`	brain or tissue mask of class nifti
`FLAIR`	volume of class nifti
`T1`	volume of class nifti
`T2`	volume of class nifti. If not available use NULL.
`PD`	volume of class nifti. If not available use NULL.
`tissue`	is a logical value that determines whether the brain mask is a full brain mask or tissue mask (excludes CSF), should be FALSE unless you provide the tissue mask as the brain_mask object
`gold_standard`	gold standard lesion segmentation mask of class nifti
`normalize`	is 'no' by default and will not perform any normalization on data. To normalize data specify 'Z' for z-score normalization or 'WS' for WhiteStripe normalization
`cand_mask`	is NULL to use candidate mask procedure proposed with method or a nifti object to be used as the candidate mask
`slices`	vector of desired slices to train on, if NULL then train over the entire brain mask
`orientation`	string value telling which orientation the training slices are specified in, can take the values of "axial", "sagittal", or "coronal"
`cores`	1 numeric indicating the number of cores to be used (no more than 4 is useful for this software implementation)
`verbose`	logical indicating printing diagnostic output