FIX: Syntax & connections

Author: mgxd

nibabies/workflows/anatomical/base.py

@@ -80,9 +80,6 @@ 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 ...utils.misc import fix_multi_source_name
     from .brain_extraction import (
@@ -270,31 +267,45 @@ def init_infant_anat_wf(
 
     # fmt:off
     wf.connect([
-        (inputnode, t1w_template_wf, [("t1w", "inputnode.in_files")]),
-        (inputnode, t2w_template_wf, [("t2w", "inputnode.in_files")]),
+        (inputnode, t1w_template_wf, [("t1w", "inputnode.anat_files")]),
+        (inputnode, t2w_template_wf, [("t2w", "inputnode.anat_files")]),
         (inputnode, anat_reports_wf, [("t1w", "inputnode.source_file")]),
         (inputnode, coreg_report_wf, [("t1w", "inputnode.source_file")]),
         (inputnode, anat_norm_wf, [(("t1w", fix_multi_source_name), "inputnode.orig_t1w")]),
 
         (t1w_template_wf, outputnode, [
-            ("outputnode.realign_xfms", "anat_ref_xfms")]),
-        (t1w_template_wf, t1w_preproc_wf, [("outputnode.out_file", "inputnode.in_anat")]),
-        (t2w_template_wf, t2w_preproc_wf, [("outputnode.out_file", "inputnode.in_anat")]),
+            ("outputnode.anat_realign_xfm", "anat_ref_xfms")]),
+        (t1w_template_wf, t1w_preproc_wf, [("outputnode.anat_ref", "inputnode.in_anat")]),
         (t1w_template_wf, anat_derivatives_wf, [
-            ("outputnode.valid_list", "inputnode.source_files"),
-            ("outputnode.realign_xfms", "inputnode.t1w_ref_xfms")]),
+            ("outputnode.anat_valid_list", "inputnode.source_files"),
+            ("outputnode.anat_realign_xfm", "inputnode.t1w_ref_xfms")]),
+        (t1w_template_wf, anat_reports_wf, [
+            ("outputnode.out_report", "inputnode.t1w_conform_report")]),
+
+        (t2w_template_wf, t2w_preproc_wf, [("outputnode.anat_ref", "inputnode.in_anat")]),
         (t2w_template_wf, anat_derivatives_wf, [
-            ("outputnode.valid_list", "inputnode.t2w_source_files")]),
+            ("outputnode.anat_valid_list", "inputnode.t2w_source_files")]),
 
         (t1w_preproc_wf, coregistration_wf, [("outputnode.anat_preproc", "inputnode.in_t1w")]),
         (t1w_preproc_wf, coreg_report_wf, [("outputnode.anat_preproc", "inputnode.t1w_preproc")]),
-        (t1w_preproc_wf, anat_norm_wf, [
-            ("outputnode.t1w_preproc", "inputnode.moving_image"),
-            ("outputnode.t1w_mask", "inputnode.moving_mask")]),
 
         (coregistration_wf, coreg_report_wf, [
             ("outputnode.t1w_mask", "inputnode.in_mask"),
             ("outputnode.t2w_preproc", "inputnode.t2w_preproc")]),
+        (coregistration_wf, anat_norm_wf, [
+            ("outputnode.t1w_preproc", "inputnode.moving_image"),
+            ("outputnode.t1w_mask", "inputnode.moving_mask")]),
+        (coregistration_wf, anat_seg_wf, [("outputnode.t1w_brain", "inputnode.anat_brain")]),
+        (coregistration_wf, anat_derivatives_wf, [
+            ("outputnode.t1w_mask", "inputnode.t1w_mask"),
+            ("outputnode.t1w_preproc", "inputnode.t1w_preproc"),
+            ("outputnode.t2w_preproc", "inputnode.t2w_preproc"),
+         ]),
+        (coregistration_wf, outputnode, [
+            ("outputnode.t1w_preproc", "anat_preproc"),
+            ("outputnode.t1w_brain", "anat_brain"),
+            ("outputnode.t1w_mask", "anat_mask"),
+        ]),
 
         (anat_seg_wf, outputnode, [
             ("outputnode.anat_dseg", "anat_dseg"),
@@ -321,20 +332,6 @@ def init_infant_anat_wf(
             ("outputnode.anat2std_xfm", "inputnode.anat2std_xfm"),
             ("outputnode.std2anat_xfm", "inputnode.std2anat_xfm")]),
 
-        (coregistration_wf, anat_seg_wf, [("outputnode.t1w_brain", "inputnode.anat_brain")]),
-        (coregistration_wf, anat_derivatives_wf, [
-            ("outputnode.t1w_mask", "inputnode.t1w_mask"),
-            ("outputnode.t1w_preproc", "inputnode.t1w_preproc"),
-            ("outputnode.t2w_preproc", "inputnode.t2w_preproc"),
-         ]),
-        (coregistration_wf, outputnode, [
-            ("outputnode.t1w_preproc", "anat_preproc"),
-            ("outputnode.t1w_brain", "anat_brain"),
-            ("outputnode.t1w_mask", "anat_mask"),
-        ]),
-
-        (t1w_template_wf, anat_reports_wf, [
-            ("outputnode.out_report", "inputnode.t1w_conform_report")]),
         (outputnode, anat_reports_wf, [
             ("anat_preproc", "inputnode.t1w_preproc"),
             ("anat_mask", "inputnode.t1w_mask"),
@@ -347,9 +344,12 @@ def init_infant_anat_wf(
     if precomp_mask:
         # Ensure the mask is conformed along with the T1w
         t1w_template_wf.inputs.inputnode.anat_mask = precomp_mask
+        # fmt:off
         wf.connect([
             (t1w_template_wf, coregistration_wf, [("outputnode.anat_mask", "inputnode.in_mask")]),
+            (t2w_preproc_wf, coregistration_wf, [("outputnode.anat_preproc", "inputnode.in_t2w")])
         ])
+        # fmt:on
     else:
         # Run brain extraction on the T2w
         brain_extraction_wf = init_infant_brain_extraction_wf(
@@ -361,7 +361,7 @@ def init_infant_anat_wf(
             sloppy=sloppy,
             debug="registration" in config.execution.debug,
         )
-
+        # fmt:off
         wf.connect([
             (t1w_preproc_wf, brain_extraction_wf, [
                 ("outputnode.anat_preproc", "inputnode.in_t1w")]),
@@ -372,13 +372,12 @@ def init_infant_anat_wf(
                 ("outputnode.out_mask", "inputnode.in_mask"),
                 ("outputnode.out_probmap", "inputnode.in_probmap")]),
         ])
+        # fmt:on
 
     if precomp_aseg:
         # Ensure the segmentation is conformed along with the T1w
         t1w_template_wf.inputs.inputnode.anat_aseg = precomp_aseg
-        wf.connect([
-            (t1w_template_wf, anat_seg_wf, [("outputnode.anat_aseg", "inputnode.anat_aseg")]),
-        ])
+        wf.connect(t1w_template_wf, "outputnode.anat_aseg", anat_seg_wf, "inputnode.anat_aseg")
 
     if not freesurfer:
         return wf
@@ -411,7 +410,7 @@ def init_infant_anat_wf(
             ("outputnode.anat_aseg", "inputnode.ants_segs"),
         ]),
         (t1w_template_wf, surface_recon_wf, [
-            ("outputnode.out_file", "inputnode.t1w"),
+            ("outputnode.anat_ref", "inputnode.t1w"),
         ]),
         (coregistration_wf, surface_recon_wf, [
             ("outputnode.t1w_brain", "inputnode.skullstripped_t1"),

nibabies/workflows/anatomical/preproc.py

@@ -1,15 +1,15 @@
-import nipype.engine.pipeline as pe
 import nipype.interfaces.utility as niu
+import nipype.pipeline.engine as pe
 from niworkflows.engine.workflows import LiterateWorkflow
 
 
-def init_preproc_anat_wf(
-    *, bspline_fitting_distance: int = 200, name: str = "preproc_anat_wf"
+def init_anat_preproc_wf(
+    *, bspline_fitting_distance: int = 200, name: str = "anat_preproc_wf"
 ) -> LiterateWorkflow:
     """Polish up raw anatomical data.
 
     This workflow accepts T1w/T2w images as inputs (either raw or a merged template) and performs:
-    - Intesity clipping
+    - Intensity clipping
     - Denoising
     - N4 Bias Field Correction
 
@@ -53,7 +53,6 @@ def init_preproc_anat_wf(
 
     # fmt:off
     wf.connect([
-        # 1. Massage T2w
         (inputnode, clip, [("in_anat", "in_file")]),
         (clip, denoise, [("out_file", "input_image")]),
         (denoise, n4_correct, [("output_image", "input_image")]),

nibabies/workflows/anatomical/registration.py

@@ -177,9 +177,10 @@ def init_coregistration_wf(
         # Generate a quick, rough mask of the T2w to be used to facilitate co-registration.
         from sdcflows.interfaces.brainmask import BrainExtraction
 
-        masker = pe.Node(BrainExtraction(), name="t2w-masker")
+        masker = pe.Node(BrainExtraction(), name="t2w_masker")
         # fmt:off
         workflow.connect([
+            (inputnode, masker, [("in_t2w", "in_file")]),
             (masker, reg_mask, [("out_mask", "in_file")]),
             (masker, refine_mask, [("out_mask", "in_file")]),
             (inputnode, apply_mask, [("in_mask", "in_mask")]),

nibabies/workflows/anatomical/segmentation.py

@@ -55,7 +55,7 @@ def init_anat_segmentations_wf(
         niu.IdentityInterface(fields=["anat_aseg", "anat_dseg", "anat_tpms"]),
         name="outputnode",
     )
-    buffernode = niu.IdentityInterface(fields=["final_aseg"], name='buffernode')
+    buffernode = pe.Node(niu.IdentityInterface(fields=["final_aseg"]), name='buffernode')
 
     wf.__desc__ = """Brain tissue segmentation of cerebrospinal fluid (CSF),
 white-matter (WM) and gray-matter (GM) was performed on """

nibabies/workflows/anatomical/template.py

@@ -86,10 +86,11 @@ def init_anat_template_wf(
     outputnode = pe.Node(
         niu.IdentityInterface(
             fields=[
-                "out_file",
-                "valid_list",
-                "realign_xfms",
+                "anat_ref",
+                "anat_valid_list",
+                "anat_realign_xfm",
                 "out_report",
+                # only if precomputed inputs
                 "anat_mask",
                 "anat_aseg",
             ],
@@ -118,7 +119,7 @@ def init_anat_template_wf(
             anat_mask_conform = pe.Node(Conform(), name='anat_mask_conform')
             # fmt:off
             wf.connect([
-                (inputnode, anat_mask_conform, [('aseg', 'in_file')]),
+                (inputnode, anat_mask_conform, [('anat_mask', 'in_file')]),
                 (anat_ref_dimensions, anat_mask_conform, [
                     ('target_zooms', 'target_zooms'),
                     ('target_shape', 'target_shape')]),
@@ -129,7 +130,7 @@ def init_anat_template_wf(
             anat_aseg_conform = pe.Node(Conform(), name='anat_aseg_conform')
             # fmt:off
             wf.connect([
-                (inputnode, anat_aseg_conform, [('aseg', 'in_file')]),
+                (inputnode, anat_aseg_conform, [('anat_aseg', 'in_file')]),
                 (anat_ref_dimensions, anat_aseg_conform, [
                     ('target_zooms', 'target_zooms'),
                     ('target_shape', 'target_shape')]),