def freesurfer_pipeline(self, **kwargs):
"""
Segments grey matter, white matter and CSF from T1 images using
SPM "NewSegment" function.
NB: Default values come from the W2MHS toolbox
"""
pipeline = self.create_pipeline(
name='segmentation',
inputs=[DatasetSpec('primary', nifti_gz_format)],
outputs=[DatasetSpec('fs_recon_all',
freesurfer_recon_all_format)],
desc="Segment white/grey matter and csf",
version=1,
citations=copy(freesurfer_cites),
**kwargs)
# FS ReconAll node
recon_all = pipeline.create_node(
interface=ReconAll(), name='recon_all',
requirements=[freesurfer_req], wall_time=2000)
recon_all.inputs.directive = 'all'
recon_all.inputs.openmp = self.runner.num_processes
# Wrapper around os.path.join
join = pipeline.create_node(interface=JoinPath(), name='join')
pipeline.connect(recon_all, 'subjects_dir', join, 'dirname')
pipeline.connect(recon_all, 'subject_id', join, 'filename')
# Connect inputs/outputs
pipeline.connect_input('primary', recon_all, 'T1_files')
pipeline.connect_output('fs_recon_all', join, 'path')
return pipeline
def build_core_nodes(self):
"""Build and connect the core nodes of the pipeline."""
import os
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
from nipype.interfaces.freesurfer.preprocess import ReconAll
from .t1_freesurfer_utils import (init_input_node, write_tsv_files)
# Nodes declaration
# =================
# Initialize the pipeline
# - Extract <image_id> (e.g. sub-CLNC01_ses-M00) T1w filename;
# - Check FOV of T1w;
# - Create <subjects_dir> folder in <WD>/<Pipeline.name>/ReconAll/<image_id>/;
# - Print begin execution message.
init_input = npe.Node(
interface=nutil.Function(
input_names=['t1w', 'recon_all_args', 'output_dir'],
output_names=['image_id', 't1w', 'flags', 'subjects_dir'],
function=init_input_node),
name='0-InitPipeline')
init_input.inputs.recon_all_args = self.parameters['recon_all_args']
init_input.inputs.output_dir = os.path.join(self.base_dir, self.name, 'ReconAll')
# Run recon-all command
# FreeSurfer segmentation will be in <subjects_dir>/<image_id>/
recon_all = npe.Node(interface=ReconAll(),
name='1-SegmentationReconAll')
recon_all.inputs.directive = 'all'
# Generate TSV files containing a summary of the regional statistics
# in <subjects_dir>/regional_measures
create_tsv = npe.Node(interface=nutil.Function(
input_names=['subjects_dir', 'image_id'],
output_names=['image_id'],
function=write_tsv_files),
name='2-CreateTsvFiles')
# Connections
# ===========
self.connect([
# Get <image_id> from input_node and print begin message
(self.input_node, init_input, [('t1w', 't1w')]),
# Run recon-all command
(init_input, recon_all, [('subjects_dir', 'subjects_dir'),
('t1w', 'T1_files'),
('image_id', 'subject_id'),
('flags', 'flags')]),
# Generate TSV files
(init_input, create_tsv, [('subjects_dir', 'subjects_dir')]),
(recon_all, create_tsv, [('subject_id', 'image_id')]),
# Output node
(recon_all, self.output_node, [('subject_id', 'image_id')]),
])
def test_ReconAll_inputs():
input_map = dict(
T1_files=dict(argstr='-i %s...', ),
T2_file=dict(
argstr='-T2 %s',
min_ver='5.3.0',
),
args=dict(argstr='%s', ),
directive=dict(
argstr='-%s',
position=0,
usedefault=True,
),
environ=dict(
nohash=True,
usedefault=True,
),
flags=dict(argstr='%s', ),
hemi=dict(argstr='-hemi %s', ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
openmp=dict(argstr='-openmp %d', ),
subject_id=dict(
argstr='-subjid %s',
usedefault=True,
),
subjects_dir=dict(
argstr='-sd %s',
genfile=True,
hash_files=False,
),
terminal_output=dict(
mandatory=True,
nohash=True,
),
)
inputs = ReconAll.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def processT1(t1_filename):
import os
import nipype.pipeline.engine as pe
import nipype.interfaces.io as nio
from nipype.interfaces.freesurfer.preprocess import ReconAll
from nipype.interfaces.freesurfer.utils import MakeAverageSubject
subject_list = ['s1', 's3']
data_dir = os.path.abspath('data')
subjects_dir = os.path.abspath('amri_freesurfer_tutorial/subjects_dir')
wf = pe.Workflow(name="l1workflow")
wf.base_dir = os.path.abspath('amri_freesurfer_tutorial/workdir')
datasource = pe.MapNode(interface=nio.DataGrabber(infields=['subject_id'],
outfields=['struct']),
name='datasource',
iterfield=['subject_id'])
datasource.inputs.base_directory = data_dir
datasource.inputs.template = '%s/%s.nii'
datasource.inputs.template_args = dict(struct=[['subject_id', 'struct']])
datasource.inputs.subject_id = subject_list
recon_all = pe.MapNode(interface=ReconAll(),
name='recon_all',
iterfield=['subject_id', 'T1_files'])
recon_all.inputs.subject_id = subject_list
if not os.path.exists(subjects_dir):
os.mkdir(subjects_dir)
recon_all.inputs.subjects_dir = subjects_dir
wf.connect(datasource, 'struct', recon_all, 'T1_files')
average = pe.Node(interface=MakeAverageSubject(), name="average")
average.inputs.subjects_dir = subjects_dir
wf.connect(recon_all, 'subject_id', average, 'subjects_ids')
wf.run("MultiProc", plugin_args={'n_procs': 4})
def test_ReconAll_inputs():
input_map = dict(T1_files=dict(argstr='-i %s...',
),
T2_file=dict(argstr='-T2 %s',
min_ver='5.3.0',
),
args=dict(argstr='%s',
),
directive=dict(argstr='-%s',
position=0,
usedefault=True,
),
environ=dict(nohash=True,
usedefault=True,
),
flags=dict(argstr='%s',
),
hemi=dict(argstr='-hemi %s',
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
openmp=dict(argstr='-openmp %d',
),
subject_id=dict(argstr='-subjid %s',
usedefault=True,
),
subjects_dir=dict(argstr='-sd %s',
genfile=True,
hash_files=False,
),
terminal_output=dict(mandatory=True,
nohash=True,
),
)
inputs = ReconAll.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def freesurfer_pipeline(self, **name_maps):
"""
Segments grey matter, white matter and CSF from T1 images using
SPM "NewSegment" function.
NB: Default values come from the W2MHS toolbox
"""
pipeline = self.new_pipeline(name='segmentation',
name_maps=name_maps,
desc="Segment white/grey matter and csf",
citations=copy(freesurfer_cites))
# FS ReconAll node
recon_all = pipeline.add(
'recon_all',
interface=ReconAll(directive='all',
openmp=self.processor.num_processes),
inputs={'T1_files': ('mag_preproc', nifti_gz_format)},
requirements=[freesurfer_req.v('5.3')],
wall_time=2000)
if self.provided('t2_coreg'):
pipeline.connect_input('t2_coreg', recon_all, 'T2_file',
nifti_gz_format)
recon_all.inputs.use_T2 = True
# Wrapper around os.path.join
pipeline.add('join',
JoinPath(),
inputs={
'dirname': (recon_all, 'subjects_dir'),
'filename': (recon_all, 'subject_id')
},
outputs={'fs_recon_all': ('path', directory_format)})
return pipeline
dcm2nii_t1 = pe.Node(DcmStack(), name="dcm2nii_t1")
dcm2nii_t1.inputs.embed_meta = True
dcm2nii_t1.plugin_args = {'submit_specs': 'request_memory = 2000'}
wf.connect(datagrabber, "t1", dcm2nii_t1, "dicom_files")
t1_rename = pe.Node(util.Rename(format_string="anat.nii.gz"),
name="t1_rename")
wf.connect(dcm2nii_t1, "out_file", t1_rename, "in_file")
ds = pe.Node(nio.DataSink(), name="t1_datasink")
ds.inputs.base_directory = '/scr/kalifornien1/data/nki_enhanced/'
ds.inputs.substitutions = [('_subject_id_', '')]
ds.inputs.regexp_substitutions = [('_others_rename[0-9]*/', '')]
wf.connect(t1_rename, "out_file", ds, "niftis.@t1")
recon_all = pe.Node(ReconAll(), name="recon_all")
recon_all.plugin_args = {'submit_specs': 'request_memory = 2500'}
recon_all.inputs.args = "-no-isrunning"
#recon_all._interface._can_resume = False
#recon_all.inputs.subjects_dir = "/scr/adenauer1/freesurfer"
wf.connect(dcm2nii_t1, "out_file", recon_all, "T1_files")
wf.connect(subjects_infosource, "subject_id", recon_all, "subject_id")
def cat(s1, s2):
import os
return os.path.join(s1, s2)
join = pe.Node(util.Function(input_names=['s1', 's2'],
output_names=['out'],
function=cat),
name="join")
infosource.iterables = [('subject_id', subject_list)]
infosource_task = pe.Node(util.IdentityInterface(fields=['session']),
name="infosource_task")
infosource_task.iterables = [('session', session_list)]
# asjust the template to thelocation of the T1
templates = {
'anat':
'/gpfs/gibbs/pi/levy_ifat/Nachshon/KPE/sub-{subject_id}/ses-{session}/anat/sub-{subject_id}_ses-{session}_T1w.nii.gz'
}
selectfiles = pe.Node(nio.SelectFiles(templates, base_directory=data_dir),
name="selectfiles")
recon_all = pe.MapNode(interface=ReconAll(),
name='recon_all',
iterfield=['subject_id'])
recon_all.inputs.subject_id = subject_list
recon_all.inputs.directive = "all"
if not os.path.exists(subjects_dir):
os.mkdir(subjects_dir)
recon_all.inputs.subjects_dir = subjects_dir #Here we use specific directory, in order to avoid crash when trying to create simlinks.
#recon_all.inputs.hippocampal_subfields_T1 = True # add hippocampal subfields
# home made Node to run hippocampus and amygdala segmentation.
## Now we should add another node that will run the hippocampal subfield script:
## Look here for instructions on how to run and wrap it in a node?
## https://surfer.nmr.mgh.harvard.edu/fswiki/HippocampalSubfieldsAndNucleiOfAmygdala
hippSeg = pe.Node(interface=CustomHippoSeg(), name='hippSeg')
def test_ReconAll_outputs():
output_map = dict(
BA_stats=dict(
altkey='BA',
loc='stats',
),
T1=dict(loc='mri', ),
annot=dict(
altkey='*annot',
loc='label',
),
aparc_a2009s_stats=dict(
altkey='aparc.a2009s',
loc='stats',
),
aparc_aseg=dict(
altkey='aparc*aseg',
loc='mri',
),
aparc_stats=dict(
altkey='aparc',
loc='stats',
),
aseg=dict(loc='mri', ),
aseg_stats=dict(
altkey='aseg',
loc='stats',
),
brain=dict(loc='mri', ),
brainmask=dict(loc='mri', ),
curv=dict(loc='surf', ),
curv_stats=dict(
altkey='curv',
loc='stats',
),
entorhinal_exvivo_stats=dict(
altkey='entorhinal_exvivo',
loc='stats',
),
filled=dict(loc='mri', ),
inflated=dict(loc='surf', ),
label=dict(
altkey='*label',
loc='label',
),
norm=dict(loc='mri', ),
nu=dict(loc='mri', ),
orig=dict(loc='mri', ),
pial=dict(loc='surf', ),
rawavg=dict(loc='mri', ),
ribbon=dict(
altkey='*ribbon',
loc='mri',
),
smoothwm=dict(loc='surf', ),
sphere=dict(loc='surf', ),
sphere_reg=dict(
altkey='sphere.reg',
loc='surf',
),
subject_id=dict(),
subjects_dir=dict(),
sulc=dict(loc='surf', ),
thickness=dict(loc='surf', ),
volume=dict(loc='surf', ),
white=dict(loc='surf', ),
wm=dict(loc='mri', ),
wmparc=dict(loc='mri', ),
wmparc_stats=dict(
altkey='wmparc',
loc='stats',
),
)
outputs = ReconAll.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_ReconAll_outputs():
output_map = dict(BA_stats=dict(altkey='BA',
loc='stats',
),
T1=dict(loc='mri',
),
annot=dict(altkey='*annot',
loc='label',
),
aparc_a2009s_stats=dict(altkey='aparc.a2009s',
loc='stats',
),
aparc_aseg=dict(altkey='aparc*aseg',
loc='mri',
),
aparc_stats=dict(altkey='aparc',
loc='stats',
),
aseg=dict(loc='mri',
),
aseg_stats=dict(altkey='aseg',
loc='stats',
),
brain=dict(loc='mri',
),
brainmask=dict(loc='mri',
),
curv=dict(loc='surf',
),
curv_stats=dict(altkey='curv',
loc='stats',
),
entorhinal_exvivo_stats=dict(altkey='entorhinal_exvivo',
loc='stats',
),
filled=dict(loc='mri',
),
inflated=dict(loc='surf',
),
label=dict(altkey='*label',
loc='label',
),
norm=dict(loc='mri',
),
nu=dict(loc='mri',
),
orig=dict(loc='mri',
),
pial=dict(loc='surf',
),
rawavg=dict(loc='mri',
),
ribbon=dict(altkey='*ribbon',
loc='mri',
),
smoothwm=dict(loc='surf',
),
sphere=dict(loc='surf',
),
sphere_reg=dict(altkey='sphere.reg',
loc='surf',
),
subject_id=dict(),
subjects_dir=dict(),
sulc=dict(loc='surf',
),
thickness=dict(loc='surf',
),
volume=dict(loc='surf',
),
white=dict(loc='surf',
),
wm=dict(loc='mri',
),
wmparc=dict(loc='mri',
),
wmparc_stats=dict(altkey='wmparc',
loc='stats',
),
)
outputs = ReconAll.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
""" running freesurfer reconall to get tissue segmentations from T1 subject_id = sd51_d01 data_dir = /data/pt_mar006/subjects free_dir = /data/pt_mar006/freesurfer Usage: $ python 02_structReconAll.py <subject_id> <data_dir> <free_dir> """ from nipype.interfaces.freesurfer.preprocess import ReconAll import os, glob, sys # subject_id and T1 scan directory: user given subject_id = sys.argv[1] data_dir = sys.argv[2] free_dir = sys.argv[3] Tscan = os.path.join(data_dir, subject_id, 'nifti/mprage', 'T1.nii.gz') # run ReconAll recon_all = ReconAll() recon_all.inputs.subject_id = subject_id recon_all.inputs.subjects_dir = free_dir recon_all.inputs.T1_files = Tscan recon_all.run()
""" running freesurfer reconall to get tissue segmentations from T1 subject_id = sd51_d01 data_dir = /data/pt_mar006/subjects free_dir = /data/pt_mar006/freesurfer Usage: $ python 02_structReconAll.py <subject_id> <data_dir> <free_dir> """ from nipype.interfaces.freesurfer.preprocess import ReconAll import os, glob, sys # subject_id and T1 scan directory: user given subject_id = sys.argv[1] data_dir = sys.argv[2] free_dir = sys.argv[3] Tscan = os.path.join(data_dir, subject_id, 'nifti/mprage', 'T1.nii.gz') # run ReconAll recon_all = ReconAll() recon_all.inputs.subject_id = subject_id recon_all.inputs.subjects_dir = free_dir recon_all.inputs.T1_files = Tscan recon_all.run()
def build_core_nodes(self):
"""Build and connect the core nodes of the pipeline."""
import os
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
from nipype.interfaces.freesurfer.preprocess import ReconAll
from .t1_freesurfer_utils import init_input_node, write_tsv_files
# Nodes declaration
# =================
# Initialize the pipeline
# - Extract <image_id> (e.g. sub-CLNC01_ses-M00) T1w filename;
# - Check FOV of T1w;
# - Create <subjects_dir> folder in <WD>/<Pipeline.name>/ReconAll/<image_id>/;
# - Print begin execution message.
init_input = npe.Node(
interface=nutil.Function(
input_names=["t1w", "recon_all_args", "output_dir"],
output_names=["image_id", "t1w", "flags", "subjects_dir"],
function=init_input_node,
),
name="0-InitPipeline",
)
init_input.inputs.recon_all_args = self.parameters["recon_all_args"]
init_input.inputs.output_dir = os.path.join(self.base_dir, self.name,
"ReconAll")
# Run recon-all command
# FreeSurfer segmentation will be in <subjects_dir>/<image_id>/
recon_all = npe.Node(interface=ReconAll(),
name="1-SegmentationReconAll")
recon_all.inputs.directive = "all"
# Generate TSV files containing a summary of the regional statistics
# in <subjects_dir>/regional_measures
create_tsv = npe.Node(
interface=nutil.Function(
input_names=["subjects_dir", "image_id"],
output_names=["image_id"],
function=write_tsv_files,
),
name="2-CreateTsvFiles",
)
# Connections
# ===========
# fmt: off
self.connect([
# Get <image_id> from input_node and print begin message
(self.input_node, init_input, [("t1w", "t1w")]),
# Run recon-all command
(
init_input,
recon_all,
[("subjects_dir", "subjects_dir"), ("t1w", "T1_files"),
("image_id", "subject_id"), ("flags", "flags")],
),
# Generate TSV files
(init_input, create_tsv, [("subjects_dir", "subjects_dir")]),
(recon_all, create_tsv, [("subject_id", "image_id")]),
# Output node
(create_tsv, self.output_node, [("image_id", "image_id")]),
])
def build_core_nodes(self):
"""Build and connect the core nodes of the pipelines.
"""
import t1_freesurfer_cross_sectional_utils as utils
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
from nipype.interfaces.freesurfer.preprocess import ReconAll
from clinica.utils.stream import cprint
# check out ReconAll version
try:
if ReconAll.version.fget.func_globals['__version__'].split(".") < ['0', '11', '0']:
raise RuntimeError('ReconAll version should at least be version of 0.11.0')
except Exception as e:
cprint(str(e))
exit(1)
# MapNode to check out if we need -cw256 for every subject, and -qcache is default for every subject.
flagnode = npe.MapNode(name='flagnode',
iterfield=['t1_list'],
interface=nutil.Function(
input_names=['t1_list', 'recon_all_args'],
output_names=['output_flags'],
function=utils.checkfov))
# MapNode to transfer every subject's flag to string.
create_flags = npe.MapNode(interface=nutil.Function(
input_names=['input_flags'],
output_names=['output_str'],
function=utils.create_flags_str),
name='create_flags_string',
iterfield=['input_flags'])
# MapNode to implement recon-all.
recon_all = npe.MapNode(interface=ReconAll(),
name='recon_all',
iterfield=['subject_id', 'T1_files', 'subjects_dir', 'flags'])
recon_all.inputs.directive = 'all'
# MapNode to create the statistical tsv file for each subject
tsvmapnode = npe.MapNode(name='tsvmapnode',
iterfield=['subject_id'],
interface=nutil.Function(
input_names=['subject_id', 'output_dir'],
output_names=[],
function=utils.write_statistics_per_subject,
imports=['import os', 'import errno']))
tsvmapnode.inputs.output_dir = self.caps_directory
# Node to create the log file doing the first step quality check
lognode = npe.Node(name='lognode',
interface=nutil.Function(
input_names=['subject_list', 'session_list', 'subject_id', 'output_dir'],
output_names=[],
function=utils.log_summary))
lognode.inputs.output_dir = self.caps_directory
# Connection
# ==========
self.connect([
# FieldMap calculation
(self.input_node, flagnode, [('recon_all_args', 'recon_all_args')]),
(self.input_node, recon_all, [('subject_dir', 'subjects_dir')]),
(self.input_node, recon_all, [('subject_id', 'subject_id')]),
(self.input_node, recon_all, [('anat_t1', 'T1_files')]),
(self.input_node, flagnode, [('anat_t1', 't1_list')]),
(self.input_node, lognode, [('subject_list', 'subject_list')]),
(self.input_node, lognode, [('session_list', 'session_list')]),
(flagnode, create_flags, [('output_flags', 'input_flags')]),
(create_flags, recon_all, [('output_str', 'flags')]),
(recon_all, tsvmapnode, [('subject_id', 'subject_id')]),
(recon_all, lognode, [('subject_id', 'subject_id')]),
(recon_all, self.output_node, [('subject_id', 'subject_id')]),
])
return self
def build_core_nodes(self):
"""Build and connect the core nodes of the pipelines.
"""
import clinica.pipelines.t1_freesurfer_cross_sectional.t1_freesurfer_cross_sectional_utils as utils
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
from nipype.interfaces.freesurfer.preprocess import ReconAll
# MapNode to check out if we need -cw256 for every subject, and -qcache is default for every subject.
flagnode = npe.MapNode(name='flagnode',
iterfield=['t1_list'],
interface=nutil.Function(
input_names=['t1_list', 'recon_all_args'],
output_names=['output_flags'],
function=utils.check_fov))
# MapNode to transfer every subject's flag to string.
create_flags = npe.MapNode(interface=nutil.Function(
input_names=['input_flags'],
output_names=['output_str'],
function=utils.create_flags_str),
name='create_flags_string',
iterfield=['input_flags'])
# MapNode to implement recon-all.
recon_all = npe.MapNode(
interface=ReconAll(),
name='recon_all',
iterfield=['subject_id', 'T1_files', 'subjects_dir', 'flags'])
recon_all.inputs.directive = 'all'
# MapNode to create the statistical tsv file for each subject
tsvmapnode = npe.MapNode(name='tsvmapnode',
iterfield=['subject_id'],
interface=nutil.Function(
input_names=['subject_id', 'output_dir'],
output_names=[],
function=utils.write_tsv_files,
imports=['import os', 'import errno']))
tsvmapnode.inputs.output_dir = self.caps_directory
print_begin_message = npe.MapNode(interface=nutil.Function(
input_names=['subject_id'], function=utils.print_begin_pipeline),
iterfield='subject_id',
name='Write-Begin_Message')
print_end_message = npe.MapNode(interface=nutil.Function(
input_names=['subject_id', 'final_file'],
function=utils.print_end_pipeline),
iterfield='subject_id',
name='Write-End_Message')
# Connection
# ==========
self.connect([
(self.input_node, print_begin_message, [('subject_id',
'subject_id')]),
(self.input_node, flagnode, [('recon_all_args', 'recon_all_args')
]),
(self.input_node, flagnode, [('anat_t1', 't1_list')]),
(flagnode, create_flags, [('output_flags', 'input_flags')]),
(create_flags, recon_all, [('output_str', 'flags')]),
(self.input_node, recon_all, [('subject_dir', 'subjects_dir')]),
(self.input_node, recon_all, [('subject_id', 'subject_id')]),
(self.input_node, recon_all, [('anat_t1', 'T1_files')]),
# (self.input_node, lognode, [('subject_list', 'subject_list')]),
# (self.input_node, lognode, [('session_list', 'session_list')]),
(recon_all, tsvmapnode, [('subject_id', 'subject_id')]),
# (recon_all, lognode, [('subject_id', 'subject_id')]),
(recon_all, self.output_node, [('subject_id', 'subject_id')]),
(self.input_node, print_end_message, [('subject_id', 'subject_id')
]),
(recon_all, print_end_message, [('subject_id', 'final_file')]),
])
return self
