def tissue_seg_EPI_template_based(wf, cfg, strat_pool, pipe_num, opt=None):
'''
{"name": "tissue_seg_EPI_template_based",
"config": ["segmentation"],
"switch": ["run"],
"option_key": ["tissue_segmentation", "using"],
"option_val": "Template_Based",
"inputs": [("desc-mean_bold",
"from-template_to-bold_mode-image_desc-linear_xfm")],
"outputs": ["space-bold_label-CSF_mask",
"space-bold_label-GM_mask",
"space-bold_label-WM_mask"]}
'''
xfm_prov = strat_pool.get_cpac_provenance(
'from-template_to-bold_mode-image_desc-linear_xfm')
reg_tool = check_prov_for_regtool(xfm_prov)
use_ants = reg_tool == 'ants'
csf_template2t1 = tissue_mask_template_to_t1('CSF', use_ants)
csf_template2t1.inputs.inputspec.tissue_mask_template = cfg[
'segmentation']['tissue_segmentation']['Template_Based']['CSF']
gm_template2t1 = tissue_mask_template_to_t1('GM', use_ants)
gm_template2t1.inputs.inputspec.tissue_mask_template = cfg[
'segmentation']['tissue_segmentation']['Template_Based']['GRAY']
wm_template2t1 = tissue_mask_template_to_t1('WM', use_ants)
wm_template2t1.inputs.inputspec.tissue_mask_template = cfg[
'segmentation']['tissue_segmentation']['Template_Based']['WHITE']
node, out = strat_pool.get_data('desc-mean_bold')
wf.connect(node, out, csf_template2t1, 'inputspec.brain')
wf.connect(node, out, gm_template2t1, 'inputspec.brain')
wf.connect(node, out, wm_template2t1, 'inputspec.brain')
node, out = \
strat_pool.get_data(
'from-template_to-bold_mode-image_desc-linear_xfm')
wf.connect(node, out,
csf_template2t1, 'inputspec.standard2highres_mat')
wf.connect(node, out,
wm_template2t1, 'inputspec.standard2highres_mat')
wf.connect(node, out,
gm_template2t1, 'inputspec.standard2highres_mat')
outputs = {
'space-bold_label-CSF_mask': (csf_template2t1,
'outputspec.segment_mask_temp2t1'),
'space-bold_label-GM_mask': (gm_template2t1,
'outputspec.segment_mask_temp2t1'),
'space-bold_label-WM_mask': (wm_template2t1,
'outputspec.segment_mask_temp2t1')
}
return (wf, outputs)
def warp_longitudinal_T1w_to_template(wf, cfg, strat_pool, pipe_num, opt=None):
'''
{"name": "warp_longitudinal_T1w_to_template",
"config": ["longitudinal_template_generation"],
"switch": ["run"],
"option_key": "None",
"option_val": "None",
"inputs": [("space-longitudinal_desc-brain_T1w",
"from-longitudinal_to-template_mode-image_xfm")],
"outputs": ["space-template_desc-brain_T1w"]}
'''
xfm_prov = strat_pool.get_cpac_provenance(
'from-longitudinal_to-template_mode-image_xfm')
reg_tool = check_prov_for_regtool(xfm_prov)
num_cpus = cfg.pipeline_setup['system_config']['max_cores_per_participant']
num_ants_cores = cfg.pipeline_setup['system_config']['num_ants_threads']
apply_xfm = apply_transform(f'warp_longitudinal_to_T1template_{pipe_num}',
reg_tool,
time_series=False,
num_cpus=num_cpus,
num_ants_cores=num_ants_cores)
if reg_tool == 'ants':
apply_xfm.inputs.inputspec.interpolation = cfg.registration_workflows[
'anatomical_registration']['registration']['ANTs']['interpolation']
elif reg_tool == 'fsl':
apply_xfm.inputs.inputspec.interpolation = cfg.registration_workflows[
'anatomical_registration']['registration']['FSL-FNIRT'][
'interpolation']
node, out = strat_pool.get_data("space-longitudinal_desc-brain_T1w")
wf.connect(node, out, apply_xfm, 'inputspec.input_image')
node, out = strat_pool.get_data("T1w_brain_template")
wf.connect(node, out, apply_xfm, 'inputspec.reference')
node, out = \
strat_pool.get_data("from-longitudinal_to-template_mode-image_xfm")
wf.connect(node, out, apply_xfm, 'inputspec.transform')
outputs = {
'space-template_desc-brain_T1w': (apply_xfm, 'outputspec.output_image')
}
return (wf, outputs)
def tissue_seg_fsl_fast(wf, cfg, strat_pool, pipe_num, opt=None):
'''
{"name": "tissue_seg_fsl_fast",
"config": ["segmentation"],
"switch": ["run"],
"option_key": ["tissue_segmentation", "using"],
"option_val": "FSL-FAST",
"inputs": [(["desc-brain_T1w", "space-longitudinal_desc-brain_T1w"],
["space-T1w_desc-brain_mask",
"space-longitudinal_desc-brain_mask"],
["from-template_to-T1w_mode-image_desc-linear_xfm",
"from-template_to-longitudinal_mode-image_desc-linear_xfm"]),
"CSF_path",
"GM_path",
"WM_path"],
"outputs": ["label-CSF_mask", "label-GM_mask", "label-WM_mask",
"label-CSF_desc-preproc_mask", "label-GM_desc-preproc_mask",
"label-WM_desc-preproc_mask",
"label-CSF_probseg", "label-GM_probseg", "label-WM_probseg",
"space-longitudinal_label-CSF_mask",
"space-longitudinal_label-GM_mask",
"space-longitudinal_label-WM_mask",
"space-longitudinal_label-CSF_desc-preproc_mask",
"space-longitudinal_label-GM_desc-preproc_mask",
"space-longitudinal_label-WM_desc-preproc_mask",
"space-longitudinal_label-CSF_probseg",
"space-longitudinal_label-GM_probseg",
"space-longitudinal_label-WM_probseg"]}
'''
# FSL-FAST
# 'tissue_class_files' output is a list of individual binary tissue masks
# triggered by 'segments' boolean input (-g or --segments)
# 'probability_maps' output is a list of individual probability maps
# triggered by 'probability_maps' boolean input (-p)
segment = pe.Node(interface=fsl.FAST(), name=f'segment_{pipe_num}')
segment.inputs.img_type = 1
segment.inputs.segments = True
segment.inputs.probability_maps = True
segment.inputs.out_basename = 'segment'
check_wm = pe.Node(name='check_wm',
interface=Function(function=check_if_file_is_empty,
input_names=['in_file'],
output_names=['out_file']))
check_gm = pe.Node(name='check_gm',
interface=Function(function=check_if_file_is_empty,
input_names=['in_file'],
output_names=['out_file']))
check_csf = pe.Node(name='check_csf', interface=Function(
function=check_if_file_is_empty, input_names=['in_file'],
output_names=['out_file']))
connect, resource = \
strat_pool.get_data(["desc-brain_T1w",
"space-longitudinal_desc-brain_T1w"],
report_fetched=True)
node, out = connect
wf.connect(node, out, segment, 'in_files')
long = ''
xfm = 'from-template_to-T1w_mode-image_desc-linear_xfm'
if 'space-longitudinal' in resource:
long = 'space-longitudinal_'
xfm = 'from-template_to-longitudinal_mode-image_desc-linear_xfm'
use_custom_threshold = cfg['segmentation']['tissue_segmentation'][
'FSL-FAST']['thresholding'][
'use'] == 'Custom'
use_priors = cfg['segmentation']['tissue_segmentation'][
'FSL-FAST']['use_priors']['run']
xfm_prov = strat_pool.get_cpac_provenance(xfm)
reg_tool = check_prov_for_regtool(xfm_prov)
process_csf = process_segment_map(f'CSF_{pipe_num}', use_priors,
use_custom_threshold, reg_tool)
process_csf.inputs.inputspec.threshold = cfg['segmentation'][
'tissue_segmentation']['FSL-FAST']['thresholding']['Custom'][
'CSF_threshold_value']
if use_priors:
node, out = strat_pool.get_data('CSF_path')
wf.connect(node, out, process_csf, 'inputspec.tissue_prior')
process_gm = process_segment_map(f'GM_{pipe_num}', use_priors,
use_custom_threshold, reg_tool)
process_gm.inputs.inputspec.threshold = cfg['segmentation'][
'tissue_segmentation']['FSL-FAST']['thresholding']['Custom'][
'GM_threshold_value']
if use_priors:
node, out = strat_pool.get_data('GM_path')
wf.connect(node, out, process_gm, 'inputspec.tissue_prior')
process_wm = process_segment_map(f'WM_{pipe_num}', use_priors,
use_custom_threshold, reg_tool)
process_wm.inputs.inputspec.threshold = cfg['segmentation'][
'tissue_segmentation']['FSL-FAST']['thresholding']['Custom'][
'WM_threshold_value']
if use_priors:
node, out = strat_pool.get_data('WM_path')
wf.connect(node, out, process_wm, 'inputspec.tissue_prior')
node, out = strat_pool.get_data(["desc-brain_T1w",
"space-longitudinal_desc-brain_T1w"])
wf.connect(node, out, process_csf, 'inputspec.brain')
wf.connect(node, out, process_gm, 'inputspec.brain')
wf.connect(node, out, process_wm, 'inputspec.brain')
node, out = strat_pool.get_data(["space-T1w_desc-brain_mask",
"space-longitudinal_desc-brain_mask"])
wf.connect(node, out, process_csf, 'inputspec.brain_mask')
wf.connect(node, out, process_gm, 'inputspec.brain_mask')
wf.connect(node, out, process_wm, 'inputspec.brain_mask')
node, out = strat_pool.get_data(xfm)
wf.connect(node, out, process_csf, 'inputspec.template_to_T1_xfm')
wf.connect(node, out, process_gm, 'inputspec.template_to_T1_xfm')
wf.connect(node, out, process_wm, 'inputspec.template_to_T1_xfm')
wf.connect(segment, ('tissue_class_files', pick_wm_class_0),
process_csf, 'inputspec.tissue_class_file')
wf.connect(segment, ('probability_maps', pick_wm_prob_0),
process_csf, 'inputspec.probability_tissue_map')
wf.connect(segment, ('tissue_class_files', pick_wm_class_1),
process_gm, 'inputspec.tissue_class_file')
wf.connect(segment, ('probability_maps', pick_wm_prob_1),
process_gm, 'inputspec.probability_tissue_map')
wf.connect(segment, ('tissue_class_files', pick_wm_class_2),
process_wm, 'inputspec.tissue_class_file')
wf.connect(segment, ('probability_maps', pick_wm_prob_2),
process_wm, 'inputspec.probability_tissue_map')
get_csf = pe.Node(util.Function(input_names=['probability_maps'],
output_names=['filename'],
function=pick_wm_prob_0),
name=f'get_csf_{pipe_num}')
wf.connect(segment, 'probability_maps', get_csf, 'probability_maps')
outputs = {
f'{long}label-CSF_probseg': (get_csf, 'filename'),
f'{long}label-GM_probseg':
(segment, ('probability_maps', pick_wm_prob_1)),
f'{long}label-WM_probseg':
(segment, ('probability_maps', pick_wm_prob_2)),
f'{long}label-CSF_mask':
(segment, ('tissue_class_files', pick_wm_class_0)),
f'{long}label-GM_mask':
(segment, ('tissue_class_files', pick_wm_class_1)),
f'{long}label-WM_mask':
(segment, ('tissue_class_files', pick_wm_class_2)),
f'{long}label-CSF_desc-preproc_mask':
(process_csf, 'outputspec.segment_mask'),
f'{long}label-GM_desc-preproc_mask':
(process_gm, 'outputspec.segment_mask'),
f'{long}label-WM_desc-preproc_mask':
(process_wm, 'outputspec.segment_mask')
}
return (wf, outputs)
def warp_timeseries_to_sym_template(wf, cfg, strat_pool, pipe_num, opt=None):
'''
Node Block:
{"name": "transform_timeseries_to_sym_template",
"config": ["voxel_mirrored_homotopic_connectivity"],
"switch": ["run"],
"option_key": "None",
"option_val": "None",
"inputs": [["desc-cleaned-sm_bold",
"desc-brain-sm_bold",
"desc-preproc-sm_bold",
"desc-sm_bold"],
"from-bold_to-symtemplate_mode-image_xfm",
"T1w_brain_template_symmetric"],
"outputs": ["space-symtemplate_desc-sm_bold"]}
'''
xfm_prov = strat_pool.get_cpac_provenance(
'from-bold_to-symtemplate_mode-image_xfm')
reg_tool = check_prov_for_regtool(xfm_prov)
num_cpus = cfg.pipeline_setup['system_config']['max_cores_per_participant']
num_ants_cores = cfg.pipeline_setup['system_config']['num_ants_threads']
apply_xfm = apply_transform(f'warp_ts_to_sym_template_{pipe_num}',
reg_tool,
time_series=True,
num_cpus=num_cpus,
num_ants_cores=num_ants_cores,
mem_gb=5.0)
if reg_tool == 'ants':
apply_xfm.inputs.inputspec.interpolation = cfg.registration_workflows[
'functional_registration']['func_registration_to_template'][
'ANTs_pipelines']['interpolation']
elif reg_tool == 'fsl':
apply_xfm.inputs.inputspec.interpolation = cfg.registration_workflows[
'functional_registration']['func_registration_to_template'][
'FNIRT_pipelines']['interpolation']
# smoothed BOLD
connect, resource = strat_pool.get_data([
"desc-cleaned-sm_bold", "desc-brain-sm_bold", "desc-preproc-sm_bold",
"desc-sm_bold"
],
report_fetched=True)
node, out = connect
wf.connect(node, out, apply_xfm, 'inputspec.input_image')
node, out = strat_pool.get_data("T1w_brain_template_symmetric")
wf.connect(node, out, apply_xfm, 'inputspec.reference')
node, out = strat_pool.get_data("from-bold_to-symtemplate_mode-image_xfm")
wf.connect(node, out, apply_xfm, 'inputspec.transform')
outputs = {
f'space-symtemplate_{resource}': (apply_xfm, 'outputspec.output_image')
}
return (wf, outputs)