skbold.utils.load_roi_mask module¶
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load_roi_mask
(roi_name, atlas_name='HarvardOxford-Cortical', lateralized=False, which_hemifield=None, threshold=0, maxprob=False, yeo_conservative=False, reg_dir=None, verbose=False)[source]¶ Loads a mask (from an atlas).
Parameters: - roi_name (str) – Name of the ROI (as specified in the FSL XML-files)
- atlas_name (str) – Name of the atlas. Choose from: ‘HarvardOxford-Cortical’, ‘HarvardOxford-Subcortical’, ‘Yeo2011’.
- lateralized (bool) – Whether to use lateralized masks (only available for Harvard- Oxford atlases). If this variable is specified, you have to specify which_hemifield too.
- which_hemifield (str) – If lateralized is True, then which hemifield should be used?
- threshold (int) – Threshold for probabilistic masks (everything below this threshold is set to zero before creating the mask).
- maxprob (bool) – Whether to select only the voxels that have the highest probability of that particular ROI for a given threshold. Setting this option to true ensures that each mask has unique voxels (substantially slows down the function, though).
- yeo_conservative (bool) – If Yeo2011 atlas is picked, whether the conservative or liberal atlas should be used.
- reg_dir (str) – Absolute path to directory with registration info (in FSL format), for if you want to automatically warp the mask to native (EPI) space!
- return_path (bool) – Whether to return the path to the ROI/mask or the loaded corresponding numpy array.
Returns: - mask ((list of) numpy-array(s)) – Boolean numpy array(s) indicating the ROI-mask(s).
- mask_names ((list of) str) – Name of the mask corresponding to the numpy-array