ENH: Tag anatomical workflows

nibabies/workflows/anatomical/apply.py

@@ -2,6 +2,7 @@
 
 import nipype.interfaces.utility as niu
 import nipype.pipeline.engine as pe
+from niworkflows.engine import tag
 from smriprep.workflows.outputs import (
     init_ds_anat_volumes_wf,
     init_ds_grayord_metrics_wf,
@@ -25,6 +26,7 @@
 LOGGER = config.loggers.workflow
 
 
+@tag('anat.apply')
 def init_infant_anat_apply_wf(
     *,
     bids_root: str,

nibabies/workflows/anatomical/brain_extraction.py

@@ -4,8 +4,10 @@
 
 from nipype.interfaces import utility as niu
 from nipype.pipeline import engine as pe
+from niworkflows.engine import tag
 
 
+@tag('anat.brain-extraction')
 def init_infant_brain_extraction_wf(
     *,
     omp_nthreads: int,

nibabies/workflows/anatomical/fit.py

@@ -5,7 +5,7 @@
 from nipype.interfaces import utility as niu
 from nipype.pipeline import engine as pe
 from niworkflows.anat.ants import init_n4_only_wf
-from niworkflows.engine.workflows import LiterateWorkflow as Workflow
+from niworkflows.engine import Workflow, tag
 from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
 from niworkflows.interfaces.header import ValidateImage
 from niworkflows.interfaces.nibabel import ApplyMask, Binarize
@@ -53,6 +53,7 @@
 LOGGER = logging.getLogger('nipype.workflow')
 
 
+@tag('anat.fit')
 def init_infant_anat_fit_wf(
     *,
     age_months: int,
@@ -1374,6 +1375,7 @@ def init_infant_anat_fit_wf(
     return workflow
 
 
+@tag('anat.fit')
 def init_infant_single_anat_fit_wf(
     *,
     age_months: int,

nibabies/workflows/anatomical/outputs.py

@@ -9,7 +9,7 @@
 
 from nipype.interfaces import utility as niu
 from nipype.pipeline import engine as pe
-from niworkflows.engine.workflows import LiterateWorkflow as Workflow
+from niworkflows.engine import Workflow, tag
 from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
 from smriprep.workflows.outputs import init_template_iterator_wf
 
@@ -21,6 +21,7 @@
 BIDS_TISSUE_ORDER = ('GM', 'WM', 'CSF')
 
 
+@tag('anat.coreg-report')
 def init_coreg_report_wf(*, output_dir, name='coreg_report_wf'):
     """
     Generate and store a report in the right location.
@@ -85,6 +86,7 @@ def init_coreg_report_wf(*, output_dir, name='coreg_report_wf'):
     return workflow
 
 
+@tag('anat.reports')
 def init_anat_reports_wf(
     *,
     spaces: SpatialReferences,
@@ -283,6 +285,7 @@ def init_anat_reports_wf(
     return workflow
 
 
+@tag('anat.derivatives')
 def init_anat_derivatives_wf(
     *,
     bids_root: Path | str,

nibabies/workflows/anatomical/preproc.py

@@ -1,13 +1,14 @@
 import nipype.interfaces.utility as niu
 import nipype.pipeline.engine as pe
-from niworkflows.engine.workflows import LiterateWorkflow
+from niworkflows.engine import Workflow, tag
 
 
+@tag('anat.preproc')
 def init_anat_preproc_wf(
     *,
     bspline_fitting_distance: int = 200,
     name: str = 'anat_preproc_wf',
-) -> LiterateWorkflow:
+) -> Workflow:
     """Polish up raw anatomical data.
 
     This workflow accepts T1w/T2w images as inputs (either raw or a merged template) and performs:
@@ -30,7 +31,7 @@
     from niworkflows.interfaces.header import ValidateImage
     from niworkflows.interfaces.nibabel import IntensityClip
 
-    wf = LiterateWorkflow(name=name)
+    wf = Workflow(name=name)
     inputnode = pe.Node(
         niu.IdentityInterface(fields=['in_anat']),
         name='inputnode',
@@ -68,10 +69,11 @@
     return wf
 
 
-def init_csf_norm_wf(name: str = 'csf_norm_wf') -> LiterateWorkflow:
+@tag('anat.csf_norm')
+def init_csf_norm_wf(name: str = 'csf_norm_wf') -> Workflow:
     """Replace low intensity voxels within the CSF mask with the median value."""
 
-    workflow = LiterateWorkflow(name=name)
+    workflow = Workflow(name=name)
     workflow.__desc__ = (
         'The CSF mask was used to normalize the anatomical template by the median of voxels '
         'within the mask.'

nibabies/workflows/anatomical/registration.py

@@ -11,7 +11,7 @@
 )
 from nipype.interfaces.ants.base import Info as ANTsInfo
 from nipype.pipeline import engine as pe
-from niworkflows.engine.workflows import LiterateWorkflow as Workflow
+from niworkflows.engine import Workflow, tag
 from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
 from smriprep.workflows.fit.registration import (
     TemplateDesc,
@@ -21,6 +21,7 @@
 )
 
 
+@tag('anat.coreg')
 def init_coregistration_wf(
     *,
     bspline_fitting_distance: int = 200,
@@ -249,6 +250,7 @@ def init_coregistration_wf(
     return workflow
 
 
+@tag('anat.coreg-derivatives')
 def init_coregister_derivatives_wf(
     *, t1w_mask: bool, t1w_aseg: bool, t2w_aseg: bool, name: str = 'coregister_derivatives_wf'
 ):
@@ -301,6 +303,7 @@ def init_coregister_derivatives_wf(
     return workflow
 
 
+@tag('anat.concat-reg')
 def init_concat_registrations_wf(
     *,
     templates,

nibabies/workflows/anatomical/resampling.py

@@ -3,7 +3,7 @@
 import nipype.interfaces.utility as niu
 import nipype.pipeline.engine as pe
 import templateflow.api as tf
-from niworkflows.engine.workflows import LiterateWorkflow
+from niworkflows.engine import Workflow, tag
 from smriprep.interfaces.workbench import SurfaceResample
 from smriprep.workflows.surfaces import init_morph_grayords_wf
 
@@ -12,11 +12,12 @@
 from nibabies.interfaces.utils import CiftiSelect
 
 
+@tag('anat.resample-surfs')
 def init_anat_fsLR_resampling_wf(
     grayord_density: ty.Literal['91k'], mcribs: bool, name='anat_fsLR_resampling_wf'
-) -> LiterateWorkflow:
+) -> Workflow:
     """Resample the surfaces into fsLR space"""
-    workflow = LiterateWorkflow(name=name)
+    workflow = Workflow(name=name)
     fslr_density = '32k' if grayord_density == '91k' else '59k'
 
     workflow.__desc__ = """\
@@ -93,20 +94,18 @@
     # resample surfaces / morphometrics to 32k
     if mcribs:
         morph_grayords_wf = init_mcribs_morph_grayords_wf(grayord_density)
-        # fmt:off
+
         workflow.connect([
             (inputnode, morph_grayords_wf, [
                 ('morphometrics', 'inputnode.morphometrics'),
                 ('surfaces', 'inputnode.surfaces'),
                 ('sphere_reg_fsLR', 'inputnode.sphere_reg')]),
             (joinnode, morph_grayords_wf, [
                 ('midthickness_fsLR', 'inputnode.midthickness_fsLR')]),
-        ])
-        # fmt:on
+        ])  # fmt:skip
     else:
         morph_grayords_wf = init_morph_grayords_wf(grayord_density)
 
-    # fmt:off
     workflow.connect([
         (inputnode, select_surfaces, [
             ('surfaces', 'surfaces'),
@@ -126,11 +125,11 @@
         (morph_grayords_wf, outputnode, [
             ('outputnode.cifti_morph', 'cifti_morph'),
             ('outputnode.cifti_metadata', 'cifti_metadata')]),
-    ])
-    # fmt:on
+    ])  # fmt:skip
     return workflow
 
 
+@tag('anat.resample-morphs-grayords')
 def init_mcribs_morph_grayords_wf(
     grayord_density: ty.Literal['91k'],  # Only 91k supported ATM
     name: str = 'morph_grayords_wf',
@@ -174,7 +173,6 @@
 
     """
     from nipype.interfaces.workbench import MetricResample
-    from niworkflows.engine.workflows import LiterateWorkflow as Workflow
     from smriprep.interfaces.cifti import GenerateDScalar
 
     from nibabies.interfaces.workbench import SurfaceVertexAreas

nibabies/workflows/anatomical/segmentation.py

@@ -7,7 +7,7 @@
 from nipype.interfaces.ants.segmentation import JointFusion
 from nipype.pipeline import engine as pe
 from niworkflows.data import load as load_nwf
-from niworkflows.engine.workflows import LiterateWorkflow as Workflow
+from niworkflows.engine import Workflow, tag
 from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
 from niworkflows.interfaces.fixes import FixHeaderRegistration as Registration
 from niworkflows.utils.connections import listify
@@ -23,6 +23,7 @@
 LOGGER = config.loggers.workflow
 
 
+@tag('anat.segmentation')
 def init_segmentation_wf(
     *,
     sloppy: bool,
@@ -129,6 +130,7 @@ def init_segmentation_wf(
     return workflow
 
 
+@tag('anat.segmentation.jlf')
 def init_jlf_wf(
     jlf_template_dir: Path,
     sloppy: bool,

nibabies/workflows/anatomical/surfaces.py

@@ -8,7 +8,7 @@
 from nipype.interfaces import utility as niu
 from nipype.interfaces.ants import N4BiasFieldCorrection
 from nipype.pipeline import engine as pe
-from niworkflows.engine.workflows import LiterateWorkflow
+from niworkflows.engine import Workflow, tag
 from niworkflows.interfaces.freesurfer import (
     PatchedLTAConvert as LTAConvert,
 )
@@ -41,6 +41,7 @@
 ]
 
 
+@tag('anat.recon')
 def init_mcribs_surface_recon_wf(
     *,
     omp_nthreads: int,
@@ -67,7 +68,7 @@
     inputnode = pe.Node(niu.IdentityInterface(fields=SURFACE_INPUTS), name='inputnode')
     outputnode = pe.Node(niu.IdentityInterface(fields=SURFACE_OUTPUTS), name='outputnode')
 
-    workflow = LiterateWorkflow(name=name)
+    workflow = Workflow(name=name)
     workflow.__desc__ = (
         'Brain surfaces were reconstructed with a modified `MCRIBReconAll` [M-CRIB-S, @mcribs]'
         'workflow, using the reference T2w and a pre-computed anatomical segmentation'
@@ -224,6 +225,7 @@
     return workflow
 
 
+@tag('anat.fslr-reg')
 def init_mcribs_dhcp_wf(*, name='mcribs_dhcp_wf'):
     """
     Generate GIFTI registration files to dhcp (42-week) space.
@@ -233,7 +235,7 @@
     """
     from smriprep.interfaces.workbench import SurfaceSphereProjectUnproject
 
-    workflow = LiterateWorkflow(name=name)
+    workflow = Workflow(name=name)
 
     inputnode = pe.Node(
         niu.IdentityInterface(['sphere_reg', 'sulc']),
@@ -294,6 +296,7 @@
     return workflow
 
 
+@tag('anat.recon')
 def init_infantfs_surface_recon_wf(
     *,
     age_months: int,
@@ -304,7 +307,7 @@
 ):
     from nibabies.interfaces.freesurfer import InfantReconAll
 
-    workflow = LiterateWorkflow(name=name)
+    workflow = Workflow(name=name)
     inputnode = pe.Node(niu.IdentityInterface(fields=SURFACE_INPUTS), name='inputnode')
     outputnode = pe.Node(niu.IdentityInterface(fields=SURFACE_OUTPUTS), name='outputnode')
 
@@ -382,6 +385,7 @@
     return workflow
 
 
+@tag('anat.midthickness')
 def init_make_midthickness_wf(
     *, omp_nthreads: int, name: str = 'make_midthickness_wf'
 ) -> pe.Workflow:
@@ -435,6 +439,7 @@
     return workflow
 
 
+@tag('anat.resample-surfs')
 def init_resample_surfaces_dhcp_wf(
     surfaces: list[str],
     grayord_density: ty.Literal['91k', '170k'],
@@ -470,7 +475,7 @@
     midthickness
         GIFTI surface mesh corresponding to the midthickness surface, resampled to fsLR
     """
-    workflow = LiterateWorkflow(name=name)
+    workflow = Workflow(name=name)
 
     fslr_density = '32k' if grayord_density == '91k' else '59k'
 

pyproject.toml

@@ -25,7 +25,7 @@ dependencies = [
     "nireports >= 23.2.0",
     "nitime",
     "nitransforms >= 24.1.1",
-    "niworkflows >= 1.12.2",
+    "niworkflows >= 1.13.1",
     "numpy >= 1.21.0",
     "packaging",
     "pandas",