def crop_and_move_datasets(subject_id, subjects_dir, fs_dir, parcellation_name, out_roi_file):
fs_dir = op.join(subjects_dir, subject_id)
cmp_config = cmp.configuration.PipelineConfiguration()
cmp_config.parcellation_scheme = "Lausanne2008"
log = cmp_config.get_logger()
pgpath = cmp_config._get_lausanne_parcellation("Lausanne2008")[parcellation_name]["node_information_graphml"]
reg_path = out_roi_file
output_dir = op.abspath(op.curdir)
iflogger.info("Cropping and moving datasets to %s" % output_dir)
ds = [
(op.join(fs_dir, "mri", "aseg.nii.gz"), op.join(output_dir, "aseg.nii.gz")),
(op.join(fs_dir, "mri", "ribbon.nii.gz"), op.join(output_dir, "ribbon.nii.gz")),
(op.join(fs_dir, "mri", "fsmask_1mm.nii.gz"), op.join(output_dir, "fsmask_1mm.nii.gz")),
(op.join(fs_dir, "label", "cc_unknown.nii.gz"), op.join(output_dir, "cc_unknown.nii.gz")),
]
ds.append((op.join(op.curdir, "ROI_%s.nii.gz" % parcellation_name), op.join(op.curdir, "ROI_HR_th.nii.gz")))
orig = op.join(fs_dir, "mri", "orig", "001.mgz")
for d in ds:
iflogger.info("Processing %s:" % d[0])
if not op.exists(d[0]):
raise Exception("File %s does not exist." % d[0])
# reslice to original volume because the roi creation with freesurfer
# changed to 256x256x256 resolution
mri_cmd = 'mri_convert -rl "%s" -rt nearest "%s" -nc "%s"' % (orig, d[0], d[1])
runCmd(mri_cmd, log)
def crop_and_move_datasets(subject_id, subjects_dir, fs_dir, parcellation_name, out_roi_file, dilation):
fs_dir = op.join(subjects_dir, subject_id)
cmp_config = cmp.configuration.PipelineConfiguration()
cmp_config.parcellation_scheme = "Lausanne2008"
log = cmp_config.get_logger()
output_dir = op.abspath(op.curdir)
iflogger.info('Cropping and moving datasets to %s', output_dir)
ds = [
(op.join(fs_dir, 'mri', 'aseg.nii.gz'),
op.abspath('aseg.nii.gz')),
(op.join(fs_dir, 'mri', 'ribbon.nii.gz'),
op.abspath('ribbon.nii.gz')),
(op.join(fs_dir, 'mri', 'fsmask_1mm.nii.gz'),
op.abspath('fsmask_1mm.nii.gz')),
(op.join(fs_dir, 'label', 'cc_unknown.nii.gz'),
op.abspath('cc_unknown.nii.gz'))
]
ds.append((op.abspath('ROI_%s.nii.gz' % parcellation_name),
op.abspath('ROI_HR_th.nii.gz')))
if dilation is True:
ds.append((op.abspath('ROIv_%s.nii.gz' % parcellation_name),
op.abspath('ROIv_HR_th.nii.gz')))
orig = op.join(fs_dir, 'mri', 'orig', '001.mgz')
for d in ds:
iflogger.info('Processing %s:', d[0])
if not op.exists(d[0]):
raise Exception('File %s does not exist.' % d[0])
# reslice to original volume because the roi creation with freesurfer
# changed to 256x256x256 resolution
mri_cmd = 'mri_convert -rl "%s" -rt nearest "%s" -nc "%s"' % (
orig, d[0], d[1])
runCmd(mri_cmd, log)
def crop_and_move_datasets(subject_id, subjects_dir, fs_dir, parcellation_name,
out_roi_file, dilation):
fs_dir = op.join(subjects_dir, subject_id)
cmp_config = cmp.configuration.PipelineConfiguration()
cmp_config.parcellation_scheme = "Lausanne2008"
log = cmp_config.get_logger()
output_dir = op.abspath(op.curdir)
iflogger.info('Cropping and moving datasets to %s', output_dir)
ds = [(op.join(fs_dir, 'mri', 'aseg.nii.gz'), op.abspath('aseg.nii.gz')),
(op.join(fs_dir, 'mri',
'ribbon.nii.gz'), op.abspath('ribbon.nii.gz')),
(op.join(fs_dir, 'mri',
'fsmask_1mm.nii.gz'), op.abspath('fsmask_1mm.nii.gz')),
(op.join(fs_dir, 'label',
'cc_unknown.nii.gz'), op.abspath('cc_unknown.nii.gz'))]
ds.append((op.abspath('ROI_%s.nii.gz' % parcellation_name),
op.abspath('ROI_HR_th.nii.gz')))
if dilation is True:
ds.append((op.abspath('ROIv_%s.nii.gz' % parcellation_name),
op.abspath('ROIv_HR_th.nii.gz')))
orig = op.join(fs_dir, 'mri', 'orig', '001.mgz')
for d in ds:
iflogger.info('Processing %s:', d[0])
if not op.exists(d[0]):
raise Exception('File %s does not exist.' % d[0])
# reslice to original volume because the roi creation with freesurfer
# changed to 256x256x256 resolution
mri_cmd = 'mri_convert -rl "%s" -rt nearest "%s" -nc "%s"' % (
orig, d[0], d[1])
runCmd(mri_cmd, log)
def crop_and_move_WM_and_GM(subjects_dir, subject_id, wm_mask_filename, roi_filename):
"""Taken from cmtk and adapted for running as isolated function in
this interface.
http://github.com/tnez/cmp/blob/master/cmp/stages/parcellation/maskcreation.py
"""
import os.path as op
import nibabel as ni
import numpy as np
fs_dir = op.join(subjects_dir,subject_id)
output_dir = op.abspath(op.curdir)
# datasets to crop and move: (from, to)
ds = [
(op.join(fs_dir, 'mri', 'fsmask_1mm.nii.gz'), op.join(output_dir, 'fsmask_1mm.nii.gz') ),
]
ds.append( (op.join(fs_dir, 'mri', 'ROIv_%s.nii.gz' % p), op.join(ouput_dir, 'ROIv_HR_th_%s.nii.gz')) )
orig = op.join(fs_dir, 'mri', 'orig', '001.mgz')
for d in ds:
log.info("Processing %s:" % d[0])
# does it exist at all?
if not op.exists(d[0]):
raise Exception('File %s does not exist.' % d[0])
# reslice to original volume because the roi creation with freesurfer
# changed to 256x256x256 resolution
mri_cmd = 'mri_convert -rl "%s" -rt nearest "%s" -nc "%s"' % (orig, d[0], d[1])
runCmd( mri_cmd,log )
def crop_and_move_WM_and_GM(subjects_dir, subject_id, wm_mask_filename, roi_filename):
"""Taken from cmtk and adapted for running as isolated function in
this interface.
http://github.com/tnez/cmp/blob/master/cmp/stages/parcellation/maskcreation.py
"""
import os.path as op
import nibabel as ni
import numpy as np
fs_dir = op.join(subjects_dir, subject_id)
output_dir = op.abspath(op.curdir)
# datasets to crop and move: (from, to)
ds = [(op.join(fs_dir, "mri", "fsmask_1mm.nii.gz"), op.join(output_dir, "fsmask_1mm.nii.gz"))]
ds.append((op.join(fs_dir, "mri", "ROIv_%s.nii.gz" % p), op.join(ouput_dir, "ROIv_HR_th_%s.nii.gz")))
orig = op.join(fs_dir, "mri", "orig", "001.mgz")
for d in ds:
log.info("Processing %s:" % d[0])
# does it exist at all?
if not op.exists(d[0]):
raise Exception("File %s does not exist." % d[0])
# reslice to original volume because the roi creation with freesurfer
# changed to 256x256x256 resolution
mri_cmd = 'mri_convert -rl "%s" -rt nearest "%s" -nc "%s"' % (orig, d[0], d[1])
runCmd(mri_cmd, log)
def crop_and_move_datasets(subject_id, subjects_dir, fs_dir, parcellation_name,
out_roi_file, dilation):
from cmp.util import runCmd
fs_dir = op.join(subjects_dir, subject_id)
cmp_config = cmp.configuration.PipelineConfiguration()
cmp_config.parcellation_scheme = "Lausanne2008"
log = cmp_config.get_logger()
output_dir = op.abspath(op.curdir)
iflogger.info("Cropping and moving datasets to %s", output_dir)
ds = [
(op.join(fs_dir, "mri", "aseg.nii.gz"), op.abspath("aseg.nii.gz")),
(op.join(fs_dir, "mri", "ribbon.nii.gz"), op.abspath("ribbon.nii.gz")),
(op.join(fs_dir, "mri",
"fsmask_1mm.nii.gz"), op.abspath("fsmask_1mm.nii.gz")),
(
op.join(fs_dir, "label", "cc_unknown.nii.gz"),
op.abspath("cc_unknown.nii.gz"),
),
]
ds.append((
op.abspath("ROI_%s.nii.gz" % parcellation_name),
op.abspath("ROI_HR_th.nii.gz"),
))
if dilation is True:
ds.append((
op.abspath("ROIv_%s.nii.gz" % parcellation_name),
op.abspath("ROIv_HR_th.nii.gz"),
))
orig = op.join(fs_dir, "mri", "orig", "001.mgz")
for d in ds:
iflogger.info("Processing %s:", d[0])
if not op.exists(d[0]):
raise Exception("File %s does not exist." % d[0])
# reslice to original volume because the roi creation with freesurfer
# changed to 256x256x256 resolution
mri_cmd = 'mri_convert -rl "%s" -rt nearest "%s" -nc "%s"' % (
orig, d[0], d[1])
runCmd(mri_cmd, log)
def create_annot_label(subject_id, subjects_dir, fs_dir, parcellation_name):
iflogger.info("Create the cortical labels necessary for our ROIs")
iflogger.info("=================================================")
fs_label_dir = op.join(op.join(subjects_dir, subject_id), 'label')
output_dir = op.abspath(op.curdir)
paths = []
cmp_config = cmp.configuration.PipelineConfiguration()
cmp_config.parcellation_scheme = "Lausanne2008"
for hemi in ['lh', 'rh']:
spath = cmp_config._get_lausanne_parcellation(
'Lausanne2008')[parcellation_name]['fs_label_subdir_name'] % hemi
paths.append(spath)
for p in paths:
try:
os.makedirs(op.join('.', p))
except:
pass
if '33' in parcellation_name:
comp = [
('rh', 'myatlas_36_rh.gcs', 'rh.myaparc_36.annot',
'regenerated_rh_36', 'myaparc_36'),
('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
'regenerated_rh_60', 'myaparc_60'),
('lh', 'myatlas_36_lh.gcs', 'lh.myaparc_36.annot',
'regenerated_lh_36', 'myaparc_36'),
('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
'regenerated_lh_60', 'myaparc_60'),
]
elif '60' in parcellation_name:
comp = [
('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
'regenerated_rh_60', 'myaparc_60'),
('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
'regenerated_lh_60', 'myaparc_60'),
]
elif '125' in parcellation_name:
comp = [
('rh', 'myatlas_125_rh.gcs', 'rh.myaparc_125.annot',
'regenerated_rh_125', 'myaparc_125'),
('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
'regenerated_rh_60', 'myaparc_60'),
('lh', 'myatlas_125_lh.gcs', 'lh.myaparc_125.annot',
'regenerated_lh_125', 'myaparc_125'),
('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
'regenerated_lh_60', 'myaparc_60'),
]
elif '250' in parcellation_name:
comp = [
('rh', 'myatlas_250_rh.gcs', 'rh.myaparc_250.annot',
'regenerated_rh_250', 'myaparc_250'),
('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
'regenerated_rh_60', 'myaparc_60'),
('lh', 'myatlas_250_lh.gcs', 'lh.myaparc_250.annot',
'regenerated_lh_250', 'myaparc_250'),
('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
'regenerated_lh_60', 'myaparc_60'),
]
else:
comp = [
('rh', 'myatlas_36_rh.gcs', 'rh.myaparc_36.annot',
'regenerated_rh_36', 'myaparc_36'),
('rh', 'myatlasP1_16_rh.gcs', 'rh.myaparcP1_16.annot',
'regenerated_rh_500', 'myaparcP1_16'),
('rh', 'myatlasP17_28_rh.gcs', 'rh.myaparcP17_28.annot',
'regenerated_rh_500', 'myaparcP17_28'),
('rh', 'myatlasP29_36_rh.gcs', 'rh.myaparcP29_36.annot',
'regenerated_rh_500', 'myaparcP29_36'),
('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
'regenerated_rh_60', 'myaparc_60'),
('rh', 'myatlas_125_rh.gcs', 'rh.myaparc_125.annot',
'regenerated_rh_125', 'myaparc_125'),
('rh', 'myatlas_250_rh.gcs', 'rh.myaparc_250.annot',
'regenerated_rh_250', 'myaparc_250'),
('lh', 'myatlas_36_lh.gcs', 'lh.myaparc_36.annot',
'regenerated_lh_36', 'myaparc_36'),
('lh', 'myatlasP1_16_lh.gcs', 'lh.myaparcP1_16.annot',
'regenerated_lh_500', 'myaparcP1_16'),
('lh', 'myatlasP17_28_lh.gcs', 'lh.myaparcP17_28.annot',
'regenerated_lh_500', 'myaparcP17_28'),
('lh', 'myatlasP29_36_lh.gcs', 'lh.myaparcP29_36.annot',
'regenerated_lh_500', 'myaparcP29_36'),
('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
'regenerated_lh_60', 'myaparc_60'),
('lh', 'myatlas_125_lh.gcs', 'lh.myaparc_125.annot',
'regenerated_lh_125', 'myaparc_125'),
('lh', 'myatlas_250_lh.gcs', 'lh.myaparc_250.annot',
'regenerated_lh_250', 'myaparc_250'),
]
log = cmp_config.get_logger()
for out in comp:
mris_cmd = 'mris_ca_label %s %s "%s/surf/%s.sphere.reg" "%s" "%s" ' % (subject_id, out[0],
op.join(subjects_dir, subject_id), out[0], cmp_config.get_lausanne_atlas(out[1]), op.join(fs_label_dir, out[2]))
runCmd(mris_cmd, log)
iflogger.info('-----------')
annot = '--annotation "%s"' % out[4]
mri_an_cmd = 'mri_annotation2label --subject %s --hemi %s --outdir "%s" %s' % (subject_id, out[0], op.join(output_dir, out[3]), annot)
iflogger.info(mri_an_cmd)
runCmd(mri_an_cmd, log)
iflogger.info('-----------')
iflogger.info(os.environ['SUBJECTS_DIR'])
# extract cc and unknown to add to tractography mask, we do not want this as a region of interest
# in FS 5.0, unknown and corpuscallosum are not available for the 35 scale (why?),
# but for the other scales only, take the ones from _60
rhun = op.join(output_dir, 'rh.unknown.label')
lhun = op.join(output_dir, 'lh.unknown.label')
rhco = op.join(output_dir, 'rh.corpuscallosum.label')
lhco = op.join(output_dir, 'lh.corpuscallosum.label')
shutil.copy(
op.join(output_dir, 'regenerated_rh_60', 'rh.unknown.label'), rhun)
shutil.copy(
op.join(output_dir, 'regenerated_lh_60', 'lh.unknown.label'), lhun)
shutil.copy(op.join(
output_dir, 'regenerated_rh_60', 'rh.corpuscallosum.label'), rhco)
shutil.copy(op.join(
output_dir, 'regenerated_lh_60', 'lh.corpuscallosum.label'), lhco)
mri_cmd = """mri_label2vol --label "%s" --label "%s" --label "%s" --label "%s" --temp "%s" --o "%s" --identity """ % (rhun, lhun, rhco, lhco, op.join(op.join(subjects_dir, subject_id), 'mri', 'orig.mgz'), op.join(fs_label_dir, 'cc_unknown.nii.gz'))
runCmd(mri_cmd, log)
runCmd('mris_volmask %s' % subject_id, log)
mri_cmd = 'mri_convert -i "%s/mri/ribbon.mgz" -o "%s/mri/ribbon.nii.gz"' % (op.join(subjects_dir, subject_id), op.join(subjects_dir, subject_id))
runCmd(mri_cmd, log)
mri_cmd = 'mri_convert -i "%s/mri/aseg.mgz" -o "%s/mri/aseg.nii.gz"' % (
op.join(subjects_dir, subject_id), op.join(subjects_dir, subject_id))
runCmd(mri_cmd, log)
iflogger.info("[ DONE ]")
def create_roi(subject_id, subjects_dir, fs_dir, parcellation_name, dilation):
""" Creates the ROI_%s.nii.gz files using the given parcellation information
from networks. Iteratively create volume. """
iflogger.info("Create the ROIs:")
output_dir = op.abspath(op.curdir)
fs_dir = op.join(subjects_dir, subject_id)
cmp_config = cmp.configuration.PipelineConfiguration()
cmp_config.parcellation_scheme = "Lausanne2008"
log = cmp_config.get_logger()
parval = cmp_config._get_lausanne_parcellation(
'Lausanne2008')[parcellation_name]
pgpath = parval['node_information_graphml']
aseg = nb.load(op.join(fs_dir, 'mri', 'aseg.nii.gz'))
asegd = aseg.get_data()
# identify cortical voxels, right (3) and left (42) hemispheres
idxr = np.where(asegd == 3)
idxl = np.where(asegd == 42)
xx = np.concatenate((idxr[0], idxl[0]))
yy = np.concatenate((idxr[1], idxl[1]))
zz = np.concatenate((idxr[2], idxl[2]))
# initialize variables necessary for cortical ROIs dilation
# dimensions of the neighbourhood for rois labels assignment (choose odd dimensions!)
shape = (25, 25, 25)
center = np.array(shape) // 2
# dist: distances from the center of the neighbourhood
dist = np.zeros(shape, dtype='float32')
for x in range(shape[0]):
for y in range(shape[1]):
for z in range(shape[2]):
distxyz = center - [x, y, z]
dist[x, y, z] = np.sqrt(np.sum(np.multiply(distxyz, distxyz)))
iflogger.info("Working on parcellation: ")
iflogger.info(cmp_config._get_lausanne_parcellation(
'Lausanne2008')[parcellation_name])
iflogger.info("========================")
pg = nx.read_graphml(pgpath)
# each node represents a brain region
# create a big 256^3 volume for storage of all ROIs
rois = np.zeros((256, 256, 256), dtype=np.int16)
count = 0
for brk, brv in pg.nodes(data=True):
count = count + 1
iflogger.info(brv)
iflogger.info(brk)
if brv['dn_hemisphere'] == 'left':
hemi = 'lh'
elif brv['dn_hemisphere'] == 'right':
hemi = 'rh'
if brv['dn_region'] == 'subcortical':
iflogger.info(brv)
iflogger.info('---------------------')
iflogger.info('Work on brain region: %s', brv['dn_region'])
iflogger.info('Freesurfer Name: %s', brv['dn_fsname'])
iflogger.info('Region %s of %s', count, pg.number_of_nodes())
iflogger.info('---------------------')
# if it is subcortical, retrieve roi from aseg
idx = np.where(asegd == int(brv['dn_fs_aseg_val']))
rois[idx] = int(brv['dn_correspondence_id'])
elif brv['dn_region'] == 'cortical':
iflogger.info(brv)
iflogger.info('---------------------')
iflogger.info('Work on brain region: %s', brv['dn_region'])
iflogger.info('Freesurfer Name: %s', brv['dn_fsname'])
iflogger.info('Region %s of %s', count, pg.number_of_nodes())
iflogger.info('---------------------')
labelpath = op.join(
output_dir, parval['fs_label_subdir_name'] % hemi)
# construct .label file name
fname = '%s.%s.label' % (hemi, brv['dn_fsname'])
# execute fs mri_label2vol to generate volume roi from the label file
# store it in temporary file to be overwritten for each region
mri_cmd = 'mri_label2vol --label "%s" --temp "%s" --o "%s" --identity' % (op.join(labelpath, fname),
op.join(fs_dir, 'mri', 'orig.mgz'), op.join(output_dir, 'tmp.nii.gz'))
runCmd(mri_cmd, log)
tmp = nb.load(op.join(output_dir, 'tmp.nii.gz'))
tmpd = tmp.get_data()
# find voxel and set them to intensityvalue in rois
idx = np.where(tmpd == 1)
rois[idx] = int(brv['dn_correspondence_id'])
# store volume eg in ROI_scale33.nii.gz
out_roi = op.abspath('ROI_%s.nii.gz' % parcellation_name)
# update the header
hdr = aseg.header
hdr2 = hdr.copy()
hdr2.set_data_dtype(np.uint16)
log.info("Save output image to %s" % out_roi)
img = nb.Nifti1Image(rois, aseg.affine, hdr2)
nb.save(img, out_roi)
iflogger.info("[ DONE ]")
# dilate cortical regions
if dilation is True:
iflogger.info("Dilating cortical regions...")
# loop throughout all the voxels belonging to the aseg GM volume
for j in range(xx.size):
if rois[xx[j], yy[j], zz[j]] == 0:
local = extract(
rois, shape, position=(xx[j], yy[j], zz[j]), fill=0)
mask = local.copy()
mask[np.nonzero(local > 0)] = 1
thisdist = np.multiply(dist, mask)
thisdist[np.nonzero(thisdist == 0)] = np.amax(thisdist)
value = np.int_(
local[np.nonzero(thisdist == np.amin(thisdist))])
if value.size > 1:
counts = np.bincount(value)
value = np.argmax(counts)
rois[xx[j], yy[j], zz[j]] = value
# store volume eg in ROIv_scale33.nii.gz
out_roi = op.abspath('ROIv_%s.nii.gz' % parcellation_name)
iflogger.info('Save output image to %s', out_roi)
img = nb.Nifti1Image(rois, aseg.affine, hdr2)
nb.save(img, out_roi)
iflogger.info("[ DONE ]")
def create_roi(subject_id, subjects_dir, fs_dir, parcellation_name, dilation):
""" Creates the ROI_%s.nii.gz files using the given parcellation information
from networks. Iteratively create volume. """
import cmp
from cmp.util import runCmd
iflogger.info("Create the ROIs:")
output_dir = op.abspath(op.curdir)
fs_dir = op.join(subjects_dir, subject_id)
cmp_config = cmp.configuration.PipelineConfiguration()
cmp_config.parcellation_scheme = "Lausanne2008"
log = cmp_config.get_logger()
parval = cmp_config._get_lausanne_parcellation(
"Lausanne2008")[parcellation_name]
pgpath = parval["node_information_graphml"]
aseg = nb.load(op.join(fs_dir, "mri", "aseg.nii.gz"))
asegd = np.asanyarray(aseg.dataobj)
# identify cortical voxels, right (3) and left (42) hemispheres
idxr = np.where(asegd == 3)
idxl = np.where(asegd == 42)
xx = np.concatenate((idxr[0], idxl[0]))
yy = np.concatenate((idxr[1], idxl[1]))
zz = np.concatenate((idxr[2], idxl[2]))
# initialize variables necessary for cortical ROIs dilation
# dimensions of the neighbourhood for rois labels assignment (choose odd dimensions!)
shape = (25, 25, 25)
center = np.array(shape) // 2
# dist: distances from the center of the neighbourhood
dist = np.zeros(shape, dtype="float32")
for x in range(shape[0]):
for y in range(shape[1]):
for z in range(shape[2]):
distxyz = center - [x, y, z]
dist[x, y, z] = np.sqrt(np.sum(np.multiply(distxyz, distxyz)))
iflogger.info("Working on parcellation: ")
iflogger.info(
cmp_config._get_lausanne_parcellation("Lausanne2008")
[parcellation_name])
iflogger.info("========================")
pg = nx.read_graphml(pgpath)
# each node represents a brain region
# create a big 256^3 volume for storage of all ROIs
rois = np.zeros((256, 256, 256), dtype=np.int16)
count = 0
for brk, brv in pg.nodes(data=True):
count = count + 1
iflogger.info(brv)
iflogger.info(brk)
if brv["dn_hemisphere"] == "left":
hemi = "lh"
elif brv["dn_hemisphere"] == "right":
hemi = "rh"
if brv["dn_region"] == "subcortical":
iflogger.info(brv)
iflogger.info("---------------------")
iflogger.info("Work on brain region: %s", brv["dn_region"])
iflogger.info("Freesurfer Name: %s", brv["dn_fsname"])
iflogger.info("Region %s of %s", count, pg.number_of_nodes())
iflogger.info("---------------------")
# if it is subcortical, retrieve roi from aseg
idx = np.where(asegd == int(brv["dn_fs_aseg_val"]))
rois[idx] = int(brv["dn_correspondence_id"])
elif brv["dn_region"] == "cortical":
iflogger.info(brv)
iflogger.info("---------------------")
iflogger.info("Work on brain region: %s", brv["dn_region"])
iflogger.info("Freesurfer Name: %s", brv["dn_fsname"])
iflogger.info("Region %s of %s", count, pg.number_of_nodes())
iflogger.info("---------------------")
labelpath = op.join(output_dir,
parval["fs_label_subdir_name"] % hemi)
# construct .label file name
fname = "%s.%s.label" % (hemi, brv["dn_fsname"])
# execute fs mri_label2vol to generate volume roi from the label file
# store it in temporary file to be overwritten for each region
mri_cmd = 'mri_label2vol --label "%s" --temp "%s" --o "%s" --identity' % (
op.join(labelpath, fname),
op.join(fs_dir, "mri", "orig.mgz"),
op.join(output_dir, "tmp.nii.gz"),
)
runCmd(mri_cmd, log)
tmp = nb.load(op.join(output_dir, "tmp.nii.gz"))
tmpd = np.asanyarray(tmp.dataobj)
# find voxel and set them to intensityvalue in rois
idx = np.where(tmpd == 1)
rois[idx] = int(brv["dn_correspondence_id"])
# store volume eg in ROI_scale33.nii.gz
out_roi = op.abspath("ROI_%s.nii.gz" % parcellation_name)
# update the header
hdr = aseg.header
hdr2 = hdr.copy()
hdr2.set_data_dtype(np.uint16)
log.info("Save output image to %s" % out_roi)
img = nb.Nifti1Image(rois, aseg.affine, hdr2)
nb.save(img, out_roi)
iflogger.info("[ DONE ]")
# dilate cortical regions
if dilation is True:
iflogger.info("Dilating cortical regions...")
# loop throughout all the voxels belonging to the aseg GM volume
for j in range(xx.size):
if rois[xx[j], yy[j], zz[j]] == 0:
local = extract(rois,
shape,
position=(xx[j], yy[j], zz[j]),
fill=0)
mask = local.copy()
mask[np.nonzero(local > 0)] = 1
thisdist = np.multiply(dist, mask)
thisdist[np.nonzero(thisdist == 0)] = np.amax(thisdist)
value = np.int_(
local[np.nonzero(thisdist == np.amin(thisdist))])
if value.size > 1:
counts = np.bincount(value)
value = np.argmax(counts)
rois[xx[j], yy[j], zz[j]] = value
# store volume eg in ROIv_scale33.nii.gz
out_roi = op.abspath("ROIv_%s.nii.gz" % parcellation_name)
iflogger.info("Save output image to %s", out_roi)
img = nb.Nifti1Image(rois, aseg.affine, hdr2)
nb.save(img, out_roi)
iflogger.info("[ DONE ]")
def create_annot_label(subject_id, subjects_dir, fs_dir, parcellation_name):
import cmp
from cmp.util import runCmd
iflogger.info("Create the cortical labels necessary for our ROIs")
iflogger.info("=================================================")
fs_label_dir = op.join(op.join(subjects_dir, subject_id), "label")
output_dir = op.abspath(op.curdir)
paths = []
cmp_config = cmp.configuration.PipelineConfiguration()
cmp_config.parcellation_scheme = "Lausanne2008"
for hemi in ["lh", "rh"]:
spath = (cmp_config._get_lausanne_parcellation("Lausanne2008")
[parcellation_name]["fs_label_subdir_name"] % hemi)
paths.append(spath)
for p in paths:
try:
os.makedirs(op.join(".", p))
except:
pass
if "33" in parcellation_name:
comp = [
(
"rh",
"myatlas_36_rh.gcs",
"rh.myaparc_36.annot",
"regenerated_rh_36",
"myaparc_36",
),
(
"rh",
"myatlas_60_rh.gcs",
"rh.myaparc_60.annot",
"regenerated_rh_60",
"myaparc_60",
),
(
"lh",
"myatlas_36_lh.gcs",
"lh.myaparc_36.annot",
"regenerated_lh_36",
"myaparc_36",
),
(
"lh",
"myatlas_60_lh.gcs",
"lh.myaparc_60.annot",
"regenerated_lh_60",
"myaparc_60",
),
]
elif "60" in parcellation_name:
comp = [
(
"rh",
"myatlas_60_rh.gcs",
"rh.myaparc_60.annot",
"regenerated_rh_60",
"myaparc_60",
),
(
"lh",
"myatlas_60_lh.gcs",
"lh.myaparc_60.annot",
"regenerated_lh_60",
"myaparc_60",
),
]
elif "125" in parcellation_name:
comp = [
(
"rh",
"myatlas_125_rh.gcs",
"rh.myaparc_125.annot",
"regenerated_rh_125",
"myaparc_125",
),
(
"rh",
"myatlas_60_rh.gcs",
"rh.myaparc_60.annot",
"regenerated_rh_60",
"myaparc_60",
),
(
"lh",
"myatlas_125_lh.gcs",
"lh.myaparc_125.annot",
"regenerated_lh_125",
"myaparc_125",
),
(
"lh",
"myatlas_60_lh.gcs",
"lh.myaparc_60.annot",
"regenerated_lh_60",
"myaparc_60",
),
]
elif "250" in parcellation_name:
comp = [
(
"rh",
"myatlas_250_rh.gcs",
"rh.myaparc_250.annot",
"regenerated_rh_250",
"myaparc_250",
),
(
"rh",
"myatlas_60_rh.gcs",
"rh.myaparc_60.annot",
"regenerated_rh_60",
"myaparc_60",
),
(
"lh",
"myatlas_250_lh.gcs",
"lh.myaparc_250.annot",
"regenerated_lh_250",
"myaparc_250",
),
(
"lh",
"myatlas_60_lh.gcs",
"lh.myaparc_60.annot",
"regenerated_lh_60",
"myaparc_60",
),
]
else:
comp = [
(
"rh",
"myatlas_36_rh.gcs",
"rh.myaparc_36.annot",
"regenerated_rh_36",
"myaparc_36",
),
(
"rh",
"myatlasP1_16_rh.gcs",
"rh.myaparcP1_16.annot",
"regenerated_rh_500",
"myaparcP1_16",
),
(
"rh",
"myatlasP17_28_rh.gcs",
"rh.myaparcP17_28.annot",
"regenerated_rh_500",
"myaparcP17_28",
),
(
"rh",
"myatlasP29_36_rh.gcs",
"rh.myaparcP29_36.annot",
"regenerated_rh_500",
"myaparcP29_36",
),
(
"rh",
"myatlas_60_rh.gcs",
"rh.myaparc_60.annot",
"regenerated_rh_60",
"myaparc_60",
),
(
"rh",
"myatlas_125_rh.gcs",
"rh.myaparc_125.annot",
"regenerated_rh_125",
"myaparc_125",
),
(
"rh",
"myatlas_250_rh.gcs",
"rh.myaparc_250.annot",
"regenerated_rh_250",
"myaparc_250",
),
(
"lh",
"myatlas_36_lh.gcs",
"lh.myaparc_36.annot",
"regenerated_lh_36",
"myaparc_36",
),
(
"lh",
"myatlasP1_16_lh.gcs",
"lh.myaparcP1_16.annot",
"regenerated_lh_500",
"myaparcP1_16",
),
(
"lh",
"myatlasP17_28_lh.gcs",
"lh.myaparcP17_28.annot",
"regenerated_lh_500",
"myaparcP17_28",
),
(
"lh",
"myatlasP29_36_lh.gcs",
"lh.myaparcP29_36.annot",
"regenerated_lh_500",
"myaparcP29_36",
),
(
"lh",
"myatlas_60_lh.gcs",
"lh.myaparc_60.annot",
"regenerated_lh_60",
"myaparc_60",
),
(
"lh",
"myatlas_125_lh.gcs",
"lh.myaparc_125.annot",
"regenerated_lh_125",
"myaparc_125",
),
(
"lh",
"myatlas_250_lh.gcs",
"lh.myaparc_250.annot",
"regenerated_lh_250",
"myaparc_250",
),
]
log = cmp_config.get_logger()
for out in comp:
mris_cmd = 'mris_ca_label %s %s "%s/surf/%s.sphere.reg" "%s" "%s" ' % (
subject_id,
out[0],
op.join(subjects_dir, subject_id),
out[0],
cmp_config.get_lausanne_atlas(out[1]),
op.join(fs_label_dir, out[2]),
)
runCmd(mris_cmd, log)
iflogger.info("-----------")
annot = '--annotation "%s"' % out[4]
mri_an_cmd = 'mri_annotation2label --subject %s --hemi %s --outdir "%s" %s' % (
subject_id,
out[0],
op.join(output_dir, out[3]),
annot,
)
iflogger.info(mri_an_cmd)
runCmd(mri_an_cmd, log)
iflogger.info("-----------")
iflogger.info(os.environ["SUBJECTS_DIR"])
# extract cc and unknown to add to tractography mask, we do not want this as a region of interest
# in FS 5.0, unknown and corpuscallosum are not available for the 35 scale (why?),
# but for the other scales only, take the ones from _60
rhun = op.join(output_dir, "rh.unknown.label")
lhun = op.join(output_dir, "lh.unknown.label")
rhco = op.join(output_dir, "rh.corpuscallosum.label")
lhco = op.join(output_dir, "lh.corpuscallosum.label")
shutil.copy(op.join(output_dir, "regenerated_rh_60", "rh.unknown.label"),
rhun)
shutil.copy(op.join(output_dir, "regenerated_lh_60", "lh.unknown.label"),
lhun)
shutil.copy(
op.join(output_dir, "regenerated_rh_60", "rh.corpuscallosum.label"),
rhco)
shutil.copy(
op.join(output_dir, "regenerated_lh_60", "lh.corpuscallosum.label"),
lhco)
mri_cmd = (
"""mri_label2vol --label "%s" --label "%s" --label "%s" --label "%s" --temp "%s" --o "%s" --identity """
% (
rhun,
lhun,
rhco,
lhco,
op.join(op.join(subjects_dir, subject_id), "mri", "orig.mgz"),
op.join(fs_label_dir, "cc_unknown.nii.gz"),
))
runCmd(mri_cmd, log)
runCmd("mris_volmask %s" % subject_id, log)
mri_cmd = 'mri_convert -i "%s/mri/ribbon.mgz" -o "%s/mri/ribbon.nii.gz"' % (
op.join(subjects_dir, subject_id),
op.join(subjects_dir, subject_id),
)
runCmd(mri_cmd, log)
mri_cmd = 'mri_convert -i "%s/mri/aseg.mgz" -o "%s/mri/aseg.nii.gz"' % (
op.join(subjects_dir, subject_id),
op.join(subjects_dir, subject_id),
)
runCmd(mri_cmd, log)
iflogger.info("[ DONE ]")
def create_annot_label(subject_id, subjects_dir, fs_dir, parcellation_name):
iflogger.info("Create the cortical labels necessary for our ROIs")
iflogger.info("=================================================")
fs_label_dir = op.join(op.join(subjects_dir, subject_id), 'label')
output_dir = op.abspath(op.curdir)
paths = []
cmp_config = cmp.configuration.PipelineConfiguration()
cmp_config.parcellation_scheme = "Lausanne2008"
for hemi in ['lh', 'rh']:
spath = cmp_config._get_lausanne_parcellation(
'Lausanne2008')[parcellation_name]['fs_label_subdir_name'] % hemi
paths.append(spath)
for p in paths:
try:
os.makedirs(op.join('.', p))
except:
pass
if '33' in parcellation_name:
comp = [
('rh', 'myatlas_36_rh.gcs', 'rh.myaparc_36.annot',
'regenerated_rh_36', 'myaparc_36'),
('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
'regenerated_rh_60', 'myaparc_60'),
('lh', 'myatlas_36_lh.gcs', 'lh.myaparc_36.annot',
'regenerated_lh_36', 'myaparc_36'),
('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
'regenerated_lh_60', 'myaparc_60'),
]
elif '60' in parcellation_name:
comp = [
('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
'regenerated_rh_60', 'myaparc_60'),
('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
'regenerated_lh_60', 'myaparc_60'),
]
elif '125' in parcellation_name:
comp = [
('rh', 'myatlas_125_rh.gcs', 'rh.myaparc_125.annot',
'regenerated_rh_125', 'myaparc_125'),
('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
'regenerated_rh_60', 'myaparc_60'),
('lh', 'myatlas_125_lh.gcs', 'lh.myaparc_125.annot',
'regenerated_lh_125', 'myaparc_125'),
('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
'regenerated_lh_60', 'myaparc_60'),
]
elif '250' in parcellation_name:
comp = [
('rh', 'myatlas_250_rh.gcs', 'rh.myaparc_250.annot',
'regenerated_rh_250', 'myaparc_250'),
('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
'regenerated_rh_60', 'myaparc_60'),
('lh', 'myatlas_250_lh.gcs', 'lh.myaparc_250.annot',
'regenerated_lh_250', 'myaparc_250'),
('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
'regenerated_lh_60', 'myaparc_60'),
]
else:
comp = [
('rh', 'myatlas_36_rh.gcs', 'rh.myaparc_36.annot',
'regenerated_rh_36', 'myaparc_36'),
('rh', 'myatlasP1_16_rh.gcs', 'rh.myaparcP1_16.annot',
'regenerated_rh_500', 'myaparcP1_16'),
('rh', 'myatlasP17_28_rh.gcs', 'rh.myaparcP17_28.annot',
'regenerated_rh_500', 'myaparcP17_28'),
('rh', 'myatlasP29_36_rh.gcs', 'rh.myaparcP29_36.annot',
'regenerated_rh_500', 'myaparcP29_36'),
('rh', 'myatlas_60_rh.gcs', 'rh.myaparc_60.annot',
'regenerated_rh_60', 'myaparc_60'),
('rh', 'myatlas_125_rh.gcs', 'rh.myaparc_125.annot',
'regenerated_rh_125', 'myaparc_125'),
('rh', 'myatlas_250_rh.gcs', 'rh.myaparc_250.annot',
'regenerated_rh_250', 'myaparc_250'),
('lh', 'myatlas_36_lh.gcs', 'lh.myaparc_36.annot',
'regenerated_lh_36', 'myaparc_36'),
('lh', 'myatlasP1_16_lh.gcs', 'lh.myaparcP1_16.annot',
'regenerated_lh_500', 'myaparcP1_16'),
('lh', 'myatlasP17_28_lh.gcs', 'lh.myaparcP17_28.annot',
'regenerated_lh_500', 'myaparcP17_28'),
('lh', 'myatlasP29_36_lh.gcs', 'lh.myaparcP29_36.annot',
'regenerated_lh_500', 'myaparcP29_36'),
('lh', 'myatlas_60_lh.gcs', 'lh.myaparc_60.annot',
'regenerated_lh_60', 'myaparc_60'),
('lh', 'myatlas_125_lh.gcs', 'lh.myaparc_125.annot',
'regenerated_lh_125', 'myaparc_125'),
('lh', 'myatlas_250_lh.gcs', 'lh.myaparc_250.annot',
'regenerated_lh_250', 'myaparc_250'),
]
log = cmp_config.get_logger()
for out in comp:
mris_cmd = 'mris_ca_label %s %s "%s/surf/%s.sphere.reg" "%s" "%s" ' % (
subject_id, out[0], op.join(subjects_dir, subject_id), out[0],
cmp_config.get_lausanne_atlas(out[1]), op.join(
fs_label_dir, out[2]))
runCmd(mris_cmd, log)
iflogger.info('-----------')
annot = '--annotation "%s"' % out[4]
mri_an_cmd = 'mri_annotation2label --subject %s --hemi %s --outdir "%s" %s' % (
subject_id, out[0], op.join(output_dir, out[3]), annot)
iflogger.info(mri_an_cmd)
runCmd(mri_an_cmd, log)
iflogger.info('-----------')
iflogger.info(os.environ['SUBJECTS_DIR'])
# extract cc and unknown to add to tractography mask, we do not want this as a region of interest
# in FS 5.0, unknown and corpuscallosum are not available for the 35 scale (why?),
# but for the other scales only, take the ones from _60
rhun = op.join(output_dir, 'rh.unknown.label')
lhun = op.join(output_dir, 'lh.unknown.label')
rhco = op.join(output_dir, 'rh.corpuscallosum.label')
lhco = op.join(output_dir, 'lh.corpuscallosum.label')
shutil.copy(op.join(output_dir, 'regenerated_rh_60', 'rh.unknown.label'),
rhun)
shutil.copy(op.join(output_dir, 'regenerated_lh_60', 'lh.unknown.label'),
lhun)
shutil.copy(
op.join(output_dir, 'regenerated_rh_60', 'rh.corpuscallosum.label'),
rhco)
shutil.copy(
op.join(output_dir, 'regenerated_lh_60', 'lh.corpuscallosum.label'),
lhco)
mri_cmd = """mri_label2vol --label "%s" --label "%s" --label "%s" --label "%s" --temp "%s" --o "%s" --identity """ % (
rhun, lhun, rhco, lhco,
op.join(op.join(subjects_dir, subject_id), 'mri',
'orig.mgz'), op.join(fs_label_dir, 'cc_unknown.nii.gz'))
runCmd(mri_cmd, log)
runCmd('mris_volmask %s' % subject_id, log)
mri_cmd = 'mri_convert -i "%s/mri/ribbon.mgz" -o "%s/mri/ribbon.nii.gz"' % (
op.join(subjects_dir, subject_id), op.join(subjects_dir, subject_id))
runCmd(mri_cmd, log)
mri_cmd = 'mri_convert -i "%s/mri/aseg.mgz" -o "%s/mri/aseg.nii.gz"' % (
op.join(subjects_dir, subject_id), op.join(subjects_dir, subject_id))
runCmd(mri_cmd, log)
iflogger.info("[ DONE ]")
def create_roi(subject_id, subjects_dir, fs_dir, parcellation_name):
""" Creates the ROI_%s.nii.gz files using the given parcellation information
from networks. Iteratively create volume. """
iflogger.info("Create the ROIs:")
output_dir = op.abspath(op.curdir)
fs_dir = op.join(subjects_dir, subject_id)
cmp_config = cmp.configuration.PipelineConfiguration()
cmp_config.parcellation_scheme = "Lausanne2008"
log = cmp_config.get_logger()
parval = cmp_config._get_lausanne_parcellation(
'Lausanne2008')[parcellation_name]
pgpath = parval['node_information_graphml']
aseg = nb.load(op.join(fs_dir, 'mri', 'aseg.nii.gz'))
asegd = aseg.get_data()
iflogger.info("Working on parcellation: ")
iflogger.info(
cmp_config._get_lausanne_parcellation('Lausanne2008')
[parcellation_name])
iflogger.info("========================")
pg = nx.read_graphml(pgpath)
# each node represents a brain region
# create a big 256^3 volume for storage of all ROIs
rois = np.zeros((256, 256, 256), dtype=np.int16)
count = 0
for brk, brv in pg.nodes_iter(data=True):
count = count + 1
iflogger.info(brv)
iflogger.info(brk)
if brv['dn_hemisphere'] == 'left':
hemi = 'lh'
elif brv['dn_hemisphere'] == 'right':
hemi = 'rh'
if brv['dn_region'] == 'subcortical':
iflogger.info(brv)
iflogger.info("---------------------")
iflogger.info("Work on brain region: %s" % (brv['dn_region']))
iflogger.info("Freesurfer Name: %s" % brv['dn_fsname'])
iflogger.info("Region %s of %s " % (count, pg.number_of_nodes()))
iflogger.info("---------------------")
# if it is subcortical, retrieve roi from aseg
idx = np.where(asegd == int(brv['dn_fs_aseg_val']))
rois[idx] = int(brv['dn_correspondence_id'])
elif brv['dn_region'] == 'cortical':
iflogger.info(brv)
iflogger.info("---------------------")
iflogger.info("Work on brain region: %s" % (brv['dn_region']))
iflogger.info("Freesurfer Name: %s" % brv['dn_fsname'])
iflogger.info("Region %s of %s " % (count, pg.number_of_nodes()))
iflogger.info("---------------------")
labelpath = op.join(output_dir,
parval['fs_label_subdir_name'] % hemi)
# construct .label file name
fname = '%s.%s.label' % (hemi, brv['dn_fsname'])
# execute fs mri_label2vol to generate volume roi from the label file
# store it in temporary file to be overwritten for each region
mri_cmd = 'mri_label2vol --label "%s" --temp "%s" --o "%s" --identity' % (
op.join(labelpath, fname), op.join(fs_dir, 'mri', 'orig.mgz'),
op.join(output_dir, 'tmp.nii.gz'))
runCmd(mri_cmd, log)
tmp = nb.load(op.join(output_dir, 'tmp.nii.gz'))
tmpd = tmp.get_data()
# find voxel and set them to intensityvalue in rois
idx = np.where(tmpd == 1)
rois[idx] = int(brv['dn_correspondence_id'])
# store volume eg in ROI_scale33.nii.gz
out_roi = op.join(output_dir, 'ROI_%s.nii.gz' % parcellation_name)
# update the header
hdr = aseg.get_header()
hdr2 = hdr.copy()
hdr2.set_data_dtype(np.uint16)
log.info("Save output image to %s" % out_roi)
img = nb.Nifti1Image(rois, aseg.get_affine(), hdr2)
nb.save(img, out_roi)
iflogger.info("[ DONE ]")
def create_roi(subject_id, subjects_dir, fs_dir, parcellation_name):
""" Creates the ROI_%s.nii.gz files using the given parcellation information
from networks. Iteratively create volume. """
iflogger.info("Create the ROIs:")
output_dir = op.abspath(op.curdir)
fs_dir = op.join(subjects_dir, subject_id)
cmp_config = cmp.configuration.PipelineConfiguration()
cmp_config.parcellation_scheme = "Lausanne2008"
log = cmp_config.get_logger()
parval = cmp_config._get_lausanne_parcellation("Lausanne2008")[parcellation_name]
pgpath = parval["node_information_graphml"]
aseg = nb.load(op.join(fs_dir, "mri", "aseg.nii.gz"))
asegd = aseg.get_data()
iflogger.info("Working on parcellation: ")
iflogger.info(cmp_config._get_lausanne_parcellation("Lausanne2008")[parcellation_name])
iflogger.info("========================")
pg = nx.read_graphml(pgpath)
# each node represents a brain region
# create a big 256^3 volume for storage of all ROIs
rois = np.zeros((256, 256, 256), dtype=np.int16)
count = 0
for brk, brv in pg.nodes_iter(data=True):
count = count + 1
iflogger.info(brv)
iflogger.info(brk)
if brv["dn_hemisphere"] == "left":
hemi = "lh"
elif brv["dn_hemisphere"] == "right":
hemi = "rh"
if brv["dn_region"] == "subcortical":
iflogger.info(brv)
iflogger.info("---------------------")
iflogger.info("Work on brain region: %s" % (brv["dn_region"]))
iflogger.info("Freesurfer Name: %s" % brv["dn_fsname"])
iflogger.info("Region %s of %s " % (count, pg.number_of_nodes()))
iflogger.info("---------------------")
# if it is subcortical, retrieve roi from aseg
idx = np.where(asegd == int(brv["dn_fs_aseg_val"]))
rois[idx] = int(brv["dn_correspondence_id"])
elif brv["dn_region"] == "cortical":
iflogger.info(brv)
iflogger.info("---------------------")
iflogger.info("Work on brain region: %s" % (brv["dn_region"]))
iflogger.info("Freesurfer Name: %s" % brv["dn_fsname"])
iflogger.info("Region %s of %s " % (count, pg.number_of_nodes()))
iflogger.info("---------------------")
labelpath = op.join(output_dir, parval["fs_label_subdir_name"] % hemi)
# construct .label file name
fname = "%s.%s.label" % (hemi, brv["dn_fsname"])
# execute fs mri_label2vol to generate volume roi from the label file
# store it in temporary file to be overwritten for each region
mri_cmd = 'mri_label2vol --label "%s" --temp "%s" --o "%s" --identity' % (
op.join(labelpath, fname),
op.join(fs_dir, "mri", "orig.mgz"),
op.join(output_dir, "tmp.nii.gz"),
)
runCmd(mri_cmd, log)
tmp = nb.load(op.join(output_dir, "tmp.nii.gz"))
tmpd = tmp.get_data()
# find voxel and set them to intensityvalue in rois
idx = np.where(tmpd == 1)
rois[idx] = int(brv["dn_correspondence_id"])
# store volume eg in ROI_scale33.nii.gz
out_roi = op.join(output_dir, "ROI_%s.nii.gz" % parcellation_name)
# update the header
hdr = aseg.get_header()
hdr2 = hdr.copy()
hdr2.set_data_dtype(np.uint16)
log.info("Save output image to %s" % out_roi)
img = nb.Nifti1Image(rois, aseg.get_affine(), hdr2)
nb.save(img, out_roi)
iflogger.info("[ DONE ]")
def generate_WM_and_GM_mask(subjects_dir, subject_id, wm_mask_filename, roi_filename):
"""Taken from cmtk and adapted for running as isolated function in
this interface.
http://github.com/tnez/cmp/blob/master/cmp/stages/parcellation/maskcreation.py
"""
import os.path as op
import nibabel as ni
import numpy as np
fs_dir = op.join(subjects_dir,subject_id)
output_dir = op.abspath(op.curdir)
# need to convert
mri_cmd = 'mri_convert -i "%s/mri/aparc+aseg.mgz" -o "%s/mri/aparc+aseg.nii.gz"' % (fs_dir, fs_dir)
runCmd( mri_cmd, None )
fout = op.join(fs_dir, 'mri', 'aparc+aseg.nii.gz') ##OUTPUT
niiAPARCimg = ni.load(fout)
niiAPARCdata = niiAPARCimg.get_data()
# mri_convert aparc+aseg.mgz aparc+aseg.nii.gz
WMout = op.join(output_dir, wm_mask_filename)
#%% label mapping
# Using FreesurferColorLUT.txt
# mappings are stored in mappings.ods
# CORTICAL = {1 : [ 1, 2, 3, 5, 6, 7, 8, 9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],
# 2 : [31,13, 9,21,27,25,19,29,15,23, 1,24, 4,30,26,11, 6, 2, 5,22,16,14,10,20,12, 7, 8,18,30,17, 3,28,33]}
#
#
# SUBCORTICAL = {1:[48,49,50,51,52,53,54,58,59,60, 9,10,11,12,13,17,18,26,27,28],
# 2:[34,34,35,36,37,40,41,38,39,39,75,75,76,77,78,81,82,79,80,80]}
#
# OTHER = {1:[16],
# 2:[83]}
MAPPING = [[1,2012],[2,2019],[3,2032],[4,2014],[5,2020],[6,2018],[7,2027],[8,2028],[9,2003],[10,2024],
[11,2017],[12,2026],[13,2002],[14,2023],[15,2010],[16,2022],[17,2031],[18,2029],[19,2008],
[20,2025],[21,2005],[22,2021],[23,2011],[24,2013],[25,2007],[26,2016],[27,2006],[28,2033],
[29,2009],[30,2015],[31,2001],[32,2030],[33,2034],[34,2035],[35,49],[36,50],[37,51],[38,52],
[39,58],[40,53],[41,54],[42,1012],[43,1019],[44,1032],[45,1014],[46,1020],[47,1018],[48,1027],
[49,1028],[50,1003],[51,1024],[52,1017],[53,1026],[54,1002],[55,1023],[56,1010],[57,1022],
[58,1031],[59,1029],[60,1008],[61,1025],[62,1005],[63,1021],[64,1011],[65,1013],[66,1007],
[67,1016],[68,1006],[69,1033],[70,1009],[71,1015],[72,1001],[73,1030],[74,1034],[75,1035],
[76,10],[77,11],[78,12],[79,13],[80,26],[81,17],[82,18],[83,16]]
WM = [2, 29, 32, 41, 61, 64, 59, 60, 27, 28] + range(77,86+1) + range(100, 117+1) + \
range(155,158+1) + range(195,196+1) + range(199,200+1) + range(203,204+1) + \
[212, 219, 223] + range(250,255+1)
# add
# 59 Right-Substancia-Nigra
# 60 Right-VentralDC
# 27 Left-Substancia-Nigra
# 28 Left-VentralDC
#%% create WM mask
niiWM = np.zeros( niiAPARCdata.shape, dtype = np.uint8 )
for i in WM:
niiWM[niiAPARCdata == i] = 1
# we do not add subcortical regions
# for i in SUBCORTICAL[1]:
# niiWM[niiAPARCdata == i] = 1
img = ni.Nifti1Image(niiWM, niiAPARCimg.get_affine(), niiAPARCimg.get_header())
ni.save(img, WMout)
#%% create GM mask (CORTICAL+SUBCORTICAL)
#% -------------------------------------
GMout = op.join(output_dir, roi_filename)
niiGM = np.zeros( niiAPARCdata.shape, dtype = np.uint8 )
for ma in MAPPING:
niiGM[ niiAPARCdata == ma[1]] = ma[0]
# # % 33 cortical regions (stored in the order of "parcel33")
# for idx,i in enumerate(CORTICAL[1]):
# niiGM[ niiAPARCdata == (2000+i)] = CORTICAL[2][idx] # RIGHT
# niiGM[ niiAPARCdata == (1000+i)] = CORTICAL[2][idx] + 41 # LEFT
# #% subcortical nuclei
# for idx,i in enumerate(SUBCORTICAL[1]):
# niiGM[ niiAPARCdata == i ] = SUBCORTICAL[2][idx]
# # % other region to account for in the GM
# for idx, i in enumerate(OTHER[1]):
# niiGM[ niiAPARCdata == i ] = OTHER[2][idx]
img = ni.Nifti1Image(niiGM, niiAPARCimg.get_affine(), niiAPARCimg.get_header())
ni.save(img, GMout)
def generate_WM_and_GM_mask(subjects_dir, subject_id, wm_mask_filename, roi_filename):
"""Taken from cmtk and adapted for running as isolated function in
this interface.
http://github.com/tnez/cmp/blob/master/cmp/stages/parcellation/maskcreation.py
"""
import os.path as op
import nibabel as ni
import numpy as np
fs_dir = op.join(subjects_dir, subject_id)
output_dir = op.abspath(op.curdir)
# need to convert
mri_cmd = 'mri_convert -i "%s/mri/aparc+aseg.mgz" -o "%s/mri/aparc+aseg.nii.gz"' % (fs_dir, fs_dir)
runCmd(mri_cmd, None)
fout = op.join(fs_dir, "mri", "aparc+aseg.nii.gz") ##OUTPUT
niiAPARCimg = ni.load(fout)
niiAPARCdata = niiAPARCimg.get_data()
# mri_convert aparc+aseg.mgz aparc+aseg.nii.gz
WMout = op.join(output_dir, wm_mask_filename)
#%% label mapping
# Using FreesurferColorLUT.txt
# mappings are stored in mappings.ods
# CORTICAL = {1 : [ 1, 2, 3, 5, 6, 7, 8, 9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],
# 2 : [31,13, 9,21,27,25,19,29,15,23, 1,24, 4,30,26,11, 6, 2, 5,22,16,14,10,20,12, 7, 8,18,30,17, 3,28,33]}
#
#
# SUBCORTICAL = {1:[48,49,50,51,52,53,54,58,59,60, 9,10,11,12,13,17,18,26,27,28],
# 2:[34,34,35,36,37,40,41,38,39,39,75,75,76,77,78,81,82,79,80,80]}
#
# OTHER = {1:[16],
# 2:[83]}
MAPPING = [
[1, 2012],
[2, 2019],
[3, 2032],
[4, 2014],
[5, 2020],
[6, 2018],
[7, 2027],
[8, 2028],
[9, 2003],
[10, 2024],
[11, 2017],
[12, 2026],
[13, 2002],
[14, 2023],
[15, 2010],
[16, 2022],
[17, 2031],
[18, 2029],
[19, 2008],
[20, 2025],
[21, 2005],
[22, 2021],
[23, 2011],
[24, 2013],
[25, 2007],
[26, 2016],
[27, 2006],
[28, 2033],
[29, 2009],
[30, 2015],
[31, 2001],
[32, 2030],
[33, 2034],
[34, 2035],
[35, 49],
[36, 50],
[37, 51],
[38, 52],
[39, 58],
[40, 53],
[41, 54],
[42, 1012],
[43, 1019],
[44, 1032],
[45, 1014],
[46, 1020],
[47, 1018],
[48, 1027],
[49, 1028],
[50, 1003],
[51, 1024],
[52, 1017],
[53, 1026],
[54, 1002],
[55, 1023],
[56, 1010],
[57, 1022],
[58, 1031],
[59, 1029],
[60, 1008],
[61, 1025],
[62, 1005],
[63, 1021],
[64, 1011],
[65, 1013],
[66, 1007],
[67, 1016],
[68, 1006],
[69, 1033],
[70, 1009],
[71, 1015],
[72, 1001],
[73, 1030],
[74, 1034],
[75, 1035],
[76, 10],
[77, 11],
[78, 12],
[79, 13],
[80, 26],
[81, 17],
[82, 18],
[83, 16],
]
WM = (
[2, 29, 32, 41, 61, 64, 59, 60, 27, 28]
+ range(77, 86 + 1)
+ range(100, 117 + 1)
+ range(155, 158 + 1)
+ range(195, 196 + 1)
+ range(199, 200 + 1)
+ range(203, 204 + 1)
+ [212, 219, 223]
+ range(250, 255 + 1)
)
# add
# 59 Right-Substancia-Nigra
# 60 Right-VentralDC
# 27 Left-Substancia-Nigra
# 28 Left-VentralDC
#%% create WM mask
niiWM = np.zeros(niiAPARCdata.shape, dtype=np.uint8)
for i in WM:
niiWM[niiAPARCdata == i] = 1
# we do not add subcortical regions
# for i in SUBCORTICAL[1]:
# niiWM[niiAPARCdata == i] = 1
img = ni.Nifti1Image(niiWM, niiAPARCimg.get_affine(), niiAPARCimg.get_header())
ni.save(img, WMout)
#%% create GM mask (CORTICAL+SUBCORTICAL)
#% -------------------------------------
GMout = op.join(output_dir, roi_filename)
niiGM = np.zeros(niiAPARCdata.shape, dtype=np.uint8)
for ma in MAPPING:
niiGM[niiAPARCdata == ma[1]] = ma[0]
# # % 33 cortical regions (stored in the order of "parcel33")
# for idx,i in enumerate(CORTICAL[1]):
# niiGM[ niiAPARCdata == (2000+i)] = CORTICAL[2][idx] # RIGHT
# niiGM[ niiAPARCdata == (1000+i)] = CORTICAL[2][idx] + 41 # LEFT
# #% subcortical nuclei
# for idx,i in enumerate(SUBCORTICAL[1]):
# niiGM[ niiAPARCdata == i ] = SUBCORTICAL[2][idx]
# # % other region to account for in the GM
# for idx, i in enumerate(OTHER[1]):
# niiGM[ niiAPARCdata == i ] = OTHER[2][idx]
img = ni.Nifti1Image(niiGM, niiAPARCimg.get_affine(), niiAPARCimg.get_header())
ni.save(img, GMout)