def extract_tissue_data(data_file,
ho_mask_file,
wm_seg_file, csf_seg_file, gm_seg_file,
wm_threshold=0.0, csf_threshold=0.0, gm_threshold=0.0):
import numpy as np
import nibabel as nb
import os
from CPAC.nuisance import erode_mask
from CPAC.utils import safe_shape
data = nb.load(data_file).get_data().astype('float64')
ho_mask = nb.load(ho_mask_file).get_data().astype('float64')
wm_seg = nb.load(wm_seg_file).get_data().astype('float64')
csf_seg = nb.load(csf_seg_file).get_data().astype('float64')
gm_seg = nb.load(gm_seg_file).get_data().astype('float64')
print 'Tissues extraction thresholds wm %d, csf %d, gm %d' % (wm_threshold,
csf_threshold,
gm_threshold)
if not safe_shape(data, ho_mask, wm_seg, csf_seg, gm_seg):
raise ValueError('Spatial dimensions for data, masks and tissues do not match')
wm_mask = erode_mask(wm_seg > wm_threshold)
# Only take the CSF at the lateral ventricals as labled in the Harvard
# Oxford parcellation regions 4 and 43
csf_mask = (csf_seg > csf_threshold)*((ho_mask==43) + (ho_mask == 4))
gm_mask = erode_mask(gm_seg > gm_threshold)
wm_sigs = data[wm_mask]
csf_sigs = data[csf_mask]
gm_sigs = data[gm_mask]
file_wm = os.path.join(os.getcwd(), 'wm_signals.npy')
file_csf = os.path.join(os.getcwd(), 'csf_signals.npy')
file_gm = os.path.join(os.getcwd(), 'gm_signals.npy')
np.save(file_wm, wm_sigs)
np.save(file_csf, csf_sigs)
np.save(file_gm, gm_sigs)
nii = nb.load(wm_seg_file)
wm_mask_file = os.path.join(os.getcwd(), 'wm_mask.nii.gz')
csf_mask_file = os.path.join(os.getcwd(), 'csf_mask.nii.gz')
gm_mask_file = os.path.join(os.getcwd(), 'gm_mask.nii.gz')
nb.Nifti1Image(wm_mask, header=nii.get_header(), affine=nii.get_affine()).to_filename(wm_mask_file)
nb.Nifti1Image(csf_mask, header=nii.get_header(), affine=nii.get_affine()).to_filename(csf_mask_file)
nb.Nifti1Image(gm_mask, header=nii.get_header(), affine=nii.get_affine()).to_filename(gm_mask_file)
return file_wm, file_csf, file_gm
def extract_tissue_data(data_file,
ventricles_mask_file,
wm_seg_file, csf_seg_file, gm_seg_file,
wm_threshold=0.0, csf_threshold=0.0, gm_threshold=0.0):
import numpy as np
import nibabel as nb
import os
from CPAC.nuisance import erode_mask
from CPAC.utils import safe_shape
print('Tissues extraction thresholds wm %d, csf %d, gm %d' % (wm_threshold,
csf_threshold,
gm_threshold))
try:
data = nb.load(data_file).get_data().astype('float64')
except:
raise MemoryError('Unable to load %s' % data_file)
try:
lat_ventricles_mask = nb.load(ventricles_mask_file).get_data().astype('float64')
except:
raise MemoryError('Unable to load %s' % lat_ventricles_mask)
if not safe_shape(data, lat_ventricles_mask):
raise ValueError('Spatial dimensions for data and the lateral ventricles mask do not match')
try:
wm_seg = nb.load(wm_seg_file).get_data().astype('float64')
except:
raise MemoryError('Unable to load %s' % wm_seg)
if not safe_shape(data, wm_seg):
raise ValueError('Spatial dimensions for data, white matter segment do not match')
wm_mask = erode_mask(wm_seg > wm_threshold)
wm_sigs = data[wm_mask]
file_wm = os.path.join(os.getcwd(), 'wm_signals.npy')
np.save(file_wm, wm_sigs)
del wm_sigs
try:
csf_seg = nb.load(csf_seg_file).get_data().astype('float64')
except:
raise MemoryError('Unable to load %s' % csf_seg)
if not safe_shape(data, csf_seg):
raise ValueError('Spatial dimensions for data, cerebral spinal fluid segment do not match')
# Only take the CSF at the lateral ventricles as labeled in the Harvard
# Oxford parcellation regions 4 and 43
csf_mask = (csf_seg > csf_threshold)*(lat_ventricles_mask==1)
csf_sigs = data[csf_mask]
file_csf = os.path.join(os.getcwd(), 'csf_signals.npy')
np.save(file_csf, csf_sigs)
del csf_sigs
try:
gm_seg = nb.load(gm_seg_file).get_data().astype('float64')
except:
raise MemoryError('Unable to load %s' % gm_seg)
if not safe_shape(data, gm_seg):
raise ValueError('Spatial dimensions for data, gray matter segment do not match')
gm_mask = erode_mask(gm_seg > gm_threshold)
gm_sigs = data[gm_mask]
file_gm = os.path.join(os.getcwd(), 'gm_signals.npy')
np.save(file_gm, gm_sigs)
del gm_sigs
nii = nb.load(wm_seg_file)
wm_mask_file = os.path.join(os.getcwd(), 'wm_mask.nii.gz')
csf_mask_file = os.path.join(os.getcwd(), 'csf_mask.nii.gz')
gm_mask_file = os.path.join(os.getcwd(), 'gm_mask.nii.gz')
nb.Nifti1Image(wm_mask, header=nii.get_header(), affine=nii.get_affine()).to_filename(wm_mask_file)
nb.Nifti1Image(csf_mask, header=nii.get_header(), affine=nii.get_affine()).to_filename(csf_mask_file)
nb.Nifti1Image(gm_mask, header=nii.get_header(), affine=nii.get_affine()).to_filename(gm_mask_file)
return file_wm, file_csf, file_gm
def extract_tissue_data(data_file,
ho_mask_file,
wm_seg_file,
csf_seg_file,
gm_seg_file,
wm_threshold=0.0,
csf_threshold=0.0,
gm_threshold=0.0):
import numpy as np
import nibabel as nb
import os
from CPAC.nuisance import erode_mask
from CPAC.utils import safe_shape
print 'Tissues extraction thresholds wm %d, csf %d, gm %d' % (
wm_threshold, csf_threshold, gm_threshold)
try:
data = nb.load(data_file).get_data().astype('float64')
except:
raise MemoryError('Unable to load %s' % data_file)
try:
ho_mask = nb.load(ho_mask_file).get_data().astype('float64')
except:
raise MemoryError('Unable to load %s' % ho_mask)
if not safe_shape(data, ho_mask):
raise ValueError(
'Spatial dimensions for data and ho_mask do not match')
try:
wm_seg = nb.load(wm_seg_file).get_data().astype('float64')
except:
raise MemoryError('Unable to load %s' % wm_seg)
if not safe_shape(data, wm_seg):
raise ValueError(
'Spatial dimensions for data, white matter segment do not match')
wm_mask = erode_mask(wm_seg > wm_threshold)
wm_sigs = data[wm_mask]
file_wm = os.path.join(os.getcwd(), 'wm_signals.npy')
np.save(file_wm, wm_sigs)
del wm_sigs
try:
csf_seg = nb.load(csf_seg_file).get_data().astype('float64')
except:
raise MemoryError('Unable to load %s' % csf_seg)
if not safe_shape(data, csf_seg):
raise ValueError(
'Spatial dimensions for data, cerebral spinal fluid segment do not match'
)
# Only take the CSF at the lateral ventricals as labled in the Harvard
# Oxford parcellation regions 4 and 43
csf_mask = (csf_seg > csf_threshold) * ((ho_mask == 43) + (ho_mask == 4))
csf_sigs = data[csf_mask]
file_csf = os.path.join(os.getcwd(), 'csf_signals.npy')
np.save(file_csf, csf_sigs)
del csf_sigs
try:
gm_seg = nb.load(gm_seg_file).get_data().astype('float64')
except:
raise MemoryError('Unable to load %s' % gm_seg)
if not safe_shape(data, gm_seg):
raise ValueError(
'Spatial dimensions for data, gray matter segment do not match')
gm_mask = erode_mask(gm_seg > gm_threshold)
gm_sigs = data[gm_mask]
file_gm = os.path.join(os.getcwd(), 'gm_signals.npy')
np.save(file_gm, gm_sigs)
del gm_sigs
nii = nb.load(wm_seg_file)
wm_mask_file = os.path.join(os.getcwd(), 'wm_mask.nii.gz')
csf_mask_file = os.path.join(os.getcwd(), 'csf_mask.nii.gz')
gm_mask_file = os.path.join(os.getcwd(), 'gm_mask.nii.gz')
nb.Nifti1Image(wm_mask, header=nii.get_header(),
affine=nii.get_affine()).to_filename(wm_mask_file)
nb.Nifti1Image(csf_mask, header=nii.get_header(),
affine=nii.get_affine()).to_filename(csf_mask_file)
nb.Nifti1Image(gm_mask, header=nii.get_header(),
affine=nii.get_affine()).to_filename(gm_mask_file)
return file_wm, file_csf, file_gm
