def init_gifti_surface_wf(name='gifti_surface_wf'):
"""
Extract surfaces from FreeSurfer derivatives folder and
re-center GIFTI coordinates to align to native T1 space
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(['subjects_dir', 'subject_id']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(['surfaces']), name='outputnode')
get_surfaces = pe.Node(nio.FreeSurferSource(), name='get_surfaces')
midthickness = pe.MapNode(
MakeMidthickness(thickness=True, distance=0.5, out_name='midthickness'),
iterfield='in_file',
name='midthickness')
save_midthickness = pe.Node(nio.DataSink(parameterization=False),
name='save_midthickness')
surface_list = pe.Node(niu.Merge(4, ravel_inputs=True),
name='surface_list', run_without_submitting=True)
fs_2_gii = pe.MapNode(fs.MRIsConvert(out_datatype='gii'),
iterfield='in_file', name='fs_2_gii')
fix_surfs = pe.MapNode(NormalizeSurf(), iterfield='in_file', name='fix_surfs')
workflow.connect([
(inputnode, get_surfaces, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(inputnode, save_midthickness, [('subjects_dir', 'base_directory'),
('subject_id', 'container')]),
# Generate midthickness surfaces and save to FreeSurfer derivatives
(get_surfaces, midthickness, [('smoothwm', 'in_file'),
('graymid', 'graymid')]),
(midthickness, save_midthickness, [('out_file', 'surf.@graymid')]),
# Produce valid GIFTI surface files (dense mesh)
(get_surfaces, surface_list, [('smoothwm', 'in1'),
('pial', 'in2'),
('inflated', 'in3')]),
(save_midthickness, surface_list, [('out_file', 'in4')]),
(surface_list, fs_2_gii, [('out', 'in_file')]),
(fs_2_gii, fix_surfs, [('converted', 'in_file')]),
(fix_surfs, outputnode, [('out_file', 'surfaces')]),
])
return workflow
def init_gifti_surface_wf(name='gifti_surface_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(['subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(['surfaces']),
name='outputnode')
get_surfaces = pe.Node(nio.FreeSurferSource(), name='get_surfaces')
midthickness = pe.MapNode(MakeMidthickness(thickness=True,
distance=0.5,
out_name='midthickness'),
iterfield='in_file',
name='midthickness')
save_midthickness = pe.Node(nio.DataSink(parameterization=False),
name='save_midthickness')
surface_list = pe.Node(niu.Merge(4, ravel_inputs=True),
name='surface_list',
run_without_submitting=True)
fs_2_gii = pe.MapNode(fs.MRIsConvert(out_datatype='gii'),
iterfield='in_file',
name='fs_2_gii')
def normalize_surfs(in_file):
""" Re-center GIFTI coordinates to fit align to native T1 space
For midthickness surfaces, add MidThickness metadata
Coordinate update based on:
https://github.com/Washington-University/workbench/blob/1b79e56/src/Algorithms/AlgorithmSurfaceApplyAffine.cxx#L73-L91
and
https://github.com/Washington-University/Pipelines/blob/ae69b9a/PostFreeSurfer/scripts/FreeSurfer2CaretConvertAndRegisterNonlinear.sh#L147
"""
import os
import numpy as np
import nibabel as nib
img = nib.load(in_file)
pointset = img.get_arrays_from_intent('NIFTI_INTENT_POINTSET')[0]
coords = pointset.data
c_ras_keys = ('VolGeomC_R', 'VolGeomC_A', 'VolGeomC_S')
ras = np.array([float(pointset.metadata[key]) for key in c_ras_keys])
# Apply C_RAS translation to coordinates
pointset.data = (coords + ras).astype(coords.dtype)
secondary = nib.gifti.GiftiNVPairs('AnatomicalStructureSecondary',
'MidThickness')
geom_type = nib.gifti.GiftiNVPairs('GeometricType', 'Anatomical')
has_ass = has_geo = False
for nvpair in pointset.meta.data:
# Remove C_RAS translation from metadata to avoid double-dipping in FreeSurfer
if nvpair.name in c_ras_keys:
nvpair.value = '0.000000'
# Check for missing metadata
elif nvpair.name == secondary.name:
has_ass = True
elif nvpair.name == geom_type.name:
has_geo = True
fname = os.path.basename(in_file)
# Update metadata for MidThickness/graymid surfaces
if 'midthickness' in fname.lower() or 'graymid' in fname.lower():
if not has_ass:
pointset.meta.data.insert(1, secondary)
if not has_geo:
pointset.meta.data.insert(2, geom_type)
img.to_filename(fname)
return os.path.abspath(fname)
fix_surfs = pe.MapNode(niu.Function(function=normalize_surfs),
iterfield='in_file',
name='fix_surfs')
workflow.connect([
(inputnode, get_surfaces, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(inputnode, save_midthickness, [('subjects_dir', 'base_directory'),
('subject_id', 'container')]),
# Generate midthickness surfaces and save to FreeSurfer derivatives
(get_surfaces, midthickness, [('smoothwm', 'in_file'),
('graymid', 'graymid')]),
(midthickness, save_midthickness, [('out_file', 'surf.@graymid')]),
# Produce valid GIFTI surface files (dense mesh)
(get_surfaces, surface_list, [('smoothwm', 'in1'), ('pial', 'in2'),
('inflated', 'in3')]),
(save_midthickness, surface_list, [('out_file', 'in4')]),
(surface_list, fs_2_gii, [('out', 'in_file')]),
(fs_2_gii, fix_surfs, [('converted', 'in_file')]),
(fix_surfs, outputnode, [('out', 'surfaces')]),
])
return workflow
def init_gifti_surface_wf(name='gifti_surface_wf'):
r"""
This workflow prepares GIFTI surfaces from a FreeSurfer subjects directory
If midthickness (or graymid) surfaces do not exist, they are generated and
saved to the subject directory as ``lh/rh.midthickness``.
These, along with the gray/white matter boundary (``lh/rh.smoothwm``), pial
sufaces (``lh/rh.pial``) and inflated surfaces (``lh/rh.inflated``) are
converted to GIFTI files.
Additionally, the vertex coordinates are :py:class:`recentered
<fmriprep.interfaces.NormalizeSurf>` to align with native T1w space.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.anatomical import init_gifti_surface_wf
wf = init_gifti_surface_wf()
**Inputs**
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
**Outputs**
surfaces
GIFTI surfaces for gray/white matter boundary, pial surface,
midthickness (or graymid) surface, and inflated surfaces
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(['subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(['surfaces']),
name='outputnode')
get_surfaces = pe.Node(nio.FreeSurferSource(), name='get_surfaces')
midthickness = pe.MapNode(MakeMidthickness(thickness=True,
distance=0.5,
out_name='midthickness'),
iterfield='in_file',
name='midthickness')
save_midthickness = pe.Node(nio.DataSink(parameterization=False),
name='save_midthickness')
surface_list = pe.Node(niu.Merge(4, ravel_inputs=True),
name='surface_list',
run_without_submitting=True)
fs_2_gii = pe.MapNode(fs.MRIsConvert(out_datatype='gii'),
iterfield='in_file',
name='fs_2_gii')
fix_surfs = pe.MapNode(NormalizeSurf(),
iterfield='in_file',
name='fix_surfs')
workflow.connect([
(inputnode, get_surfaces, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(inputnode, save_midthickness, [('subjects_dir', 'base_directory'),
('subject_id', 'container')]),
# Generate midthickness surfaces and save to FreeSurfer derivatives
(get_surfaces, midthickness, [('smoothwm', 'in_file'),
('graymid', 'graymid')]),
(midthickness, save_midthickness, [('out_file', 'surf.@graymid')]),
# Produce valid GIFTI surface files (dense mesh)
(get_surfaces, surface_list, [('smoothwm', 'in1'), ('pial', 'in2'),
('inflated', 'in3')]),
(save_midthickness, surface_list, [('out_file', 'in4')]),
(surface_list, fs_2_gii, [('out', 'in_file')]),
(fs_2_gii, fix_surfs, [('converted', 'in_file')]),
(fix_surfs, outputnode, [('out_file', 'surfaces')]),
])
return workflow
def init_refine_brainmask_wf(name='refine_brainmask'):
"""
This workflow refines the brainmask implicit in the FreeSurfer's ``aseg.mgz``
brain tissue segmentation to reconcile ANTs' and FreeSurfer's brain masks.
First, the ``aseg.mgz`` mask from FreeSurfer is refined in two
steps, using binary morphological operations:
1. With a binary closing operation the sulci are included
into the mask. This results in a smoother brain mask
that does not exclude deep, wide sulci.
2. Fill any holes (typically, there could be a hole next to
the pineal gland and the corpora quadrigemina if the great
cerebral brain is segmented out).
Second, the brain mask is grown, including pixels that have a high likelihood
to the GM tissue distribution:
3. Dilate and substract the brain mask, defining the region to search for candidate
pixels that likely belong to cortical GM.
4. Pixels found in the search region that are labeled as GM by ANTs
(during ``antsBrainExtraction.sh``) are directly added to the new mask.
5. Otherwise, estimate GM tissue parameters locally in patches of ``ww`` size,
and test the likelihood of the pixel to belong in the GM distribution.
This procedure is inspired on mindboggle's solution to the problem:
https://github.com/nipy/mindboggle/blob/master/mindboggle/guts/segment.py#L1660
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.anatomical import init_refine_brainmask_wf
wf = init_refine_brainmask_wf()
**Inputs**
in_file
Anatomical, merged T1w image after INU correction
ants_segs
Brain tissue segmentation from ANTS ``antsBrainExtraction.sh``
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
**Outputs**
out_file
New, refined brain mask
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface([
'in_file', 'ants_segs', 'subjects_dir', 'subject_id']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(['out_file']), name='outputnode')
get_aseg = pe.Node(nio.FreeSurferSource(), name='get_aseg')
tonative = pe.Node(fs.Label2Vol(), name='tonative')
tonii = pe.Node(fs.MRIConvert(out_type='niigz', resample_type='nearest'), name='tonii')
refine = pe.Node(RefineBrainMask(), name='refine')
workflow.connect([
(inputnode, refine, [('in_file', 'in_anat'),
('ants_segs', 'in_ants')]),
(inputnode, get_aseg, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(inputnode, tonii, [('in_file', 'reslice_like')]),
(get_aseg, tonative, [('aseg', 'seg_file'),
('rawavg', 'template_file'),
('aseg', 'reg_header')]),
(tonative, tonii, [('vol_label_file', 'in_file')]),
(tonii, refine, [('out_file', 'in_aseg')]),
(refine, outputnode, [('out_file', 'out_file')]),
])
return workflow
