Source code for cortex.anat

"""Contains functions for making a whitematter mask
"""
import shutil
import tempfile
import subprocess as sp

import numpy as np

#from . import utils
from .database import db
from .options import config
from .xfm import Transform

fsl_prefix = config.get('basic', 'fsl_prefix')


[docs] def brainmask(outfile, subject): raw = db.get_anat(subject, type='raw').get_filename() print('Brain masking anatomical...') cmd = '{fsl_prefix}bet {raw} {bet} -B -v'.format(fsl_prefix=fsl_prefix, raw=raw, bet=outfile) print("Calling: %s"%cmd) assert sp.call(cmd, shell=True) == 0, "Error calling fsl-bet"
[docs] def whitematter(outfile, subject, do_voxelize=False): try: if not do_voxelize: raise IOError else: voxelize(outfile, subject, surf="wm") except IOError: import nibabel try: cache = tempfile.mkdtemp() print ("Attempting to segment the brain with freesurfer...") bet2 = db.get_anat(subject, type='raw_wm').get_filename() vol = nibabel.load('{bet2}'.format(bet2=bet2)) vol_data = vol.get_fdata() print(vol_data.shape) new_data = vol_data.copy() new_data[new_data==250] = 0 new_data[new_data>0] = 1 wm_freesurf = nibabel.Nifti1Image(new_data, vol.affine, header=vol.header) wm_freesurf.to_filename(outfile) except: cache = tempfile.mkdtemp() print("Attempt with freesurfer failed, trying again with FSL...") bet = db.get_anat(subject, type='brainmask').get_filename() cmd = '{fsl_prefix}fast -o {cache}/fast {bet}'.format(fsl_prefix=fsl_prefix, cache=cache, bet=bet) assert sp.call(cmd, shell=True) == 0, "Error calling fsl-fast" wmfl = 'fast_pve_2' arr = np.asarray(nibabel.load('{cache}/{wmseg}.nii.gz'.format(cache=cache,wmseg=wmfl)).get_fdata()) if arr.sum() == 0: from warnings import warn warn('"fsl-fast" with default settings failed. Trying no pve, no bias correction...') cmd = '{fsl_prefix}fast -g --nopve --nobias -o {cache}/fast {bet}'.format(fsl_prefix=fsl_prefix, cache=cache, bet=bet) assert sp.call(cmd, shell=True) == 0, "Error calling fsl-fast" wmfl = 'fast_seg_2' cmd = '{fsl_prefix}fslmaths {cache}/{wmfl} -thr 0.5 -bin {out}'.format(fsl_prefix=fsl_prefix, cache=cache, wmfl=wmfl, out=outfile) assert sp.call(cmd, shell=True) == 0, 'Error calling fsl-maths' # check generated mask succeeded arr = np.asarray(nibabel.load('{outfl}'.format(outfl=outfile)).get_fdata()) assert arr.sum() >= 0, 'Error with generated whitematter mask.' finally: shutil.rmtree(cache)
[docs] def voxelize(outfile, subject, surf='wm', mp=True): '''Voxelize the whitematter surface to generate the white matter mask''' import nibabel from . import polyutils nib = db.get_anat(subject, "raw") shape = nib.get_shape() vox = np.zeros(shape, dtype=bool) for pts, polys in db.get_surf(subject, surf, nudge=False): xfm = Transform(np.linalg.inv(nib.affine), nib) vox += polyutils.voxelize(xfm(pts), polys, shape=shape, center=(0,0,0), mp=mp).astype('bool') nib = nibabel.Nifti1Image(vox, nib.affine, header=nib.header) nib.to_filename(outfile) return vox.T