use SDCFlows brain extraction workflow for T2w when T1w has precomputed mask

Author: madisoth

nibabies/workflows/anatomical/base.py

@@ -77,8 +77,9 @@ def init_infant_anat_wf(
     """
     from nipype.interfaces.ants.base import Info as ANTsInfo
     from niworkflows.engine.workflows import LiterateWorkflow as Workflow
-
-    from sdcflows.workflows.ancillary import init_brainextraction_wf as init_sdc_brain_extraction_wf
+    from sdcflows.workflows.ancillary import (
+        init_brainextraction_wf as init_sdc_brain_extraction_wf,
+    )
 
     from ...utils.misc import fix_multi_source_name
     from .brain_extraction import (

nibabies/workflows/anatomical/outputs.py

@@ -313,7 +313,7 @@ def init_anat_derivatives_wf(
     t2w_source_files
         List of input T2w images
     t2w_preproc
-        The T2w image in T1w space. 
+        The T2w image in T1w space.
     """
     from niworkflows.interfaces.nibabel import ApplyMask
     from niworkflows.interfaces.utility import KeySelect
@@ -376,7 +376,9 @@ def init_anat_derivatives_wf(
     )
 
     ds_t2w_preproc = pe.Node(
-        DerivativesDataSink(data_dtype="i2", base_directory=output_dir, space="T1w", desc="preproc", compress=True),
+        DerivativesDataSink(
+            data_dtype="i2", base_directory=output_dir, space="T1w", desc="preproc", compress=True
+        ),
         name="ds_t2w_preproc",
         run_without_submitting=True,
     )