fsqc.evaluateFornixSegmentation

This module provides a function to evaluate potential missegmentation of the fornix

fsqc.evaluateFornixSegmentation.evaluateFornixSegmentation(SUBJECT, SUBJECTS_DIR, OUTPUT_DIR, CREATE_SCREENSHOT=True, SCREENSHOTS_OUTFILE=None, RUN_SHAPEDNA=True, N_EIGEN=15, WRITE_EIGEN=True)

Evaluate potential missegmentation of the fornix.

This script assesses the potential missegmentation of the fornix, which might erroneously be attached to the ‘corpus callosum’ label.

It applies the cc_up.lta transform to the norm.mgz and aseg files, creating a binary corpus callosum mask and surface. The resulting files are saved to subject-specific directories within the ‘fornix’ subdirectory of the output directory.

If the corresponding arguments are set to ‘True’, the script also creates screenshots and runs a shape analysis of the corpus callosum surface. Resulting files are saved to the same directory as indicated above.

Parameters:

SUBJECTstr

The subject identifier.

SUBJECTS_DIRstr

The directory containing subject data.

OUTPUT_DIRstr

The output directory.

CREATE_SCREENSHOTbool, optional (default: True)

Whether to create screenshots.

SCREENSHOTS_OUTFILEstr or list, optional (default: None)

File or list of files for screenshots.

RUN_SHAPEDNAbool, optional (default: True)

Whether to run shape analysis.

N_EIGENint, optional (default: 30)

Number of Eigenvalues for shape analysis.

WRITE_EIGENbool, optional (default: True)

Write csv file with eigenvalues (or nans) to output directory.

Returns:

numpy.ndarray

A numpy array of N_EIGEN eigenvalues if RUN_SHAPEDNA is True, otherwise a numpy array of NaNs of the same dimension.