MAIN: workflow tidying

Author: eilidhmacnicol

nirodents/workflows/brainextraction.py

@@ -54,7 +54,7 @@ def init_rodent_brain_extraction_wf(
         Run an extra step to refine the brain mask using a brain-tissue segmentation with Atropos.
 
     """
-    wf = pe.Workflow(name)
+    
 
     if omp_nthreads is None or omp_nthreads < 1:
         omp_nthreads = cpu_count()
@@ -113,6 +113,12 @@ def init_rodent_brain_extraction_wf(
     mrg_tmpl = pe.Node(niu.Merge(2), name='mrg_tmpl')
     # mrg_tmpl.inputs.in1 = tpl_target_path
 
+    #create integration nodes to allow compatability between pipelines
+    integrate_1 = pe.Node(niu.Function(function=_integrate, 
+        input_names=['in_file'], output_names=['out_file']), name='integrate_1')
+    integrate_2 = pe.Node(niu.Function(function=_integrate, 
+        input_names=['in_file'], output_names=['out_file']), name='integrate_2')
+
     # Initialize transforms with antsAI
     init_aff = pe.Node(AI(
         metric=('Mattes', 32, 'Regular', 0.5), #0.25
@@ -131,8 +137,8 @@ def init_rodent_brain_extraction_wf(
     #     init_aff.inputs.search_grid = (1, (1, 2, 2))
 
     # Initial warping of template mask to subject space
-    warp_mask = pe.Node(ApplyTransforms(
-        interpolation='Linear', invert_transform_flags=True), name='warp_mask')
+    warp_mask_1 = pe.Node(ApplyTransforms(
+        interpolation='Linear', invert_transform_flags=True), name='warp_mask_1')
 
     fixed_mask_trait = 'fixed_image_mask'
     moving_mask_trait = 'moving_image_mask'
@@ -168,9 +174,9 @@ def init_rodent_brain_extraction_wf(
     mrg_init_transforms = pe.Node(niu.Merge(2), name='mrg_init_transforms')
 
     # Use more precise transforms to warp mask to subject space
-    warp_mask_final = pe.Node(ApplyTransforms(
+    warp_mask_2 = pe.Node(ApplyTransforms(
         interpolation='Linear', invert_transform_flags=[False, True]),
-        name='warp_mask_final')
+        name='warp_mask_2')
 
     # morphological closing of warped mask
     close_mask = pe.Node(MaskTool(), name = 'close_mask')
@@ -186,21 +192,23 @@ def init_rodent_brain_extraction_wf(
 
     # Normalise skull-stripped image to brain template
     final_settings_file = f'data/brainextraction_{final_normalization_quality}_{modality}.json'
-    final_norm = pe.Node(Registration(from_file=pkgr_fn(
+    refine_norm = pe.Node(Registration(from_file=pkgr_fn(
         'nirodents', final_settings_file)),
-        name='final_norm',
+        name='refine_norm',
         n_procs=omp_nthreads,
         mem_gb=mem_gb)
-    final_norm.inputs.float = use_float
+    refine_norm.inputs.float = use_float
 
     split_final_transforms = pe.Node(niu.Split(splits=[1,1]), name='split_final_transforms')
     mrg_final_transforms = pe.Node(niu.Merge(2), name='mrg_final_transforms')
 
-    warp_seg_mask = pe.Node(ApplyTransforms(
+    warp_mask_out = pe.Node(ApplyTransforms(
         interpolation='Linear', invert_transform_flags=[False, True]),
-        name='warp_seg_mask')
+        name='warp_mask_out')
     if tpl_brain_mask:
-        warp_seg_mask.inputs.input_image = tpl_brain_mask
+        warp_mask_out.inputs.input_image = tpl_brain_mask
+    else:
+        warp_mask_out.inputs.input_image = tpl_regmask_path
 
     warp_seg_labels =pe.Node(ApplyTransforms(
         interpolation='Linear', invert_transform_flags=[False, True]), 
@@ -216,153 +224,114 @@ def init_rodent_brain_extraction_wf(
 
     sinker = pe.Node(DataSink(), name='sinker')
 
+    #workflow definitions
+    #target image specific workflows
+    tar_prep = pe.Workflow('tar_prep')
     if modality.lower() == 't2w':
-        wf.connect([
-            # resampling, truncation, initial N4, and creation of laplacian
+        tar_prep.connect([
+            # truncation, resampling, and initial N4
             (inputnode, trunc, [('in_files', 'op1')]),
             (trunc, res_target, [(('output_image', _pop), 'in_file')]),
             (res_target, inu_n4, [('out_file', 'input_image')]),
-
-            # dilation of input mask
-            (inputnode, dil_mask, [('in_mask', 'in_file')]),
-
-            # ants AI inputs
-            (inu_n4, init_aff, [(('output_image', _pop), 'moving_image')]),
-            (dil_mask, init_aff, [('out_file', 'fixed_image_mask')]),
-            (res_tmpl, init_aff, [('out_file', 'fixed_image')]),
-
-            # warp mask to individual space
-            (dil_mask, warp_mask, [('out_file', 'input_image')]),
-            (trunc, warp_mask, [(('output_image', _pop), 'reference_image')]),
-            (init_aff, warp_mask, [('output_transform', 'transforms')]),
+            (inu_n4, integrate_1, [(('output_image', _pop), 'in_file')]),
 
             # masked N4 correction
             (trunc, inu_n4_final, [(('output_image', _pop), 'input_image')]),
-            (warp_mask, inu_n4_final, [('output_image', 'weight_image')]),
+            (inu_n4_final, integrate_2, [(('output_image', _pop), 'in_file')]),
 
             # merge laplacian and original images
             (inu_n4_final, lap_target, [(('output_image', _pop), 'op1')]),
             (lap_target, norm_lap_target, [('output_image', 'op1')]),
             (norm_lap_target, mrg_target, [('output_image', 'in2')]),
             (inu_n4_final, res_target2, [(('output_image', _pop), 'in_file')]),
             (res_target2, mrg_target, [('out_file', 'in1')]),
-
-            (res_tmpl, mrg_tmpl, [('out_file', 'in1')]),
-            (lap_tmpl, norm_lap_tmpl, [('output_image', 'op1')]),
-            (norm_lap_tmpl, mrg_tmpl, [('output_image', 'in2')]),
-
-            # normalisation inputs
-            (init_aff, init_norm, [('output_transform', 'initial_moving_transform')]),
-            (warp_mask, init_norm, [('output_image', 'moving_image_masks')]),
-            (dil_mask, init_norm, [('out_file', 'fixed_image_masks')]),
-            (mrg_tmpl, init_norm, [('out', 'fixed_image')]),
-            (mrg_target, init_norm, [('out', 'moving_image')]),
-
-            # organise normalisation outputs to warp mask
-            (init_norm, split_init_transforms, [('reverse_transforms', 'inlist')]),
-            (split_init_transforms, mrg_init_transforms, [('out2', 'in1')]),
-            (split_init_transforms, mrg_init_transforms, [('out1', 'in2')]),
-
-            (mrg_init_transforms, warp_mask_final, [('out', 'transforms')]),
-            (inu_n4_final, warp_mask_final, [(('output_image', _pop), 'reference_image')]),
-            (dil_mask, warp_mask_final, [('out_file', 'input_image')]),
-            (warp_mask_final, close_mask, [('output_image', 'in_file')]),
-            (close_mask, sinker, [('out_file', 'derivatives.@out_mask')]),
-
-            # mask brains
-            (inu_n4_final, skullstrip_tar, [(('output_image', _pop), 'in_file')]),
-            (close_mask, skullstrip_tar, [('out_file', 'in_mask')]),
-            (inputnode, skullstrip_tpl, [('in_mask', 'in_mask')]),
-
-            # final_normalisation
-            (skullstrip_tpl, final_norm, [('out_file', 'fixed_image')]),
-            (skullstrip_tar, final_norm, [('out_file', 'moving_image')]),
-
-            # Warp mask and labels to subject-space
-            (final_norm, split_final_transforms, [('reverse_transforms', 'inlist')]),
-            (split_final_transforms, mrg_final_transforms, [('out2', 'in1')]),
-            (split_final_transforms, mrg_final_transforms, [('out1', 'in2')]),
-
-            (mrg_final_transforms, warp_seg_mask, [('out', 'transforms')]),
-            (skullstrip_tar, warp_seg_mask, [('out_file', 'reference_image')]),
-            (mrg_final_transforms, warp_seg_labels, [('out', 'transforms')]),
-            (skullstrip_tar, warp_seg_labels, [('out_file', 'reference_image')]),
-
-            # Segmentation
-            (skullstrip_tar, segment, [('out_file', 'intensity_images')]),
-            (warp_seg_labels, segment, [('output_image', 'prior_image')]),
-            (warp_seg_mask, segment, [('output_image', 'mask_image')])
         ])
-        return wf
-
     elif modality == 'mp2rage':
-        wf.connect([
-            # resampling and creation of laplacians
+        tar_prep.connect([
+            # resampling and laplacian; no truncation or N4
             (inputnode, res_target, [('in_files', 'in_file')]),
             (inputnode, lap_target, [('in_files', 'op1')]),
             (lap_target, norm_lap_target, [('output_image', 'op1')]),
             (norm_lap_target, mrg_target, [('output_image', 'in2')]), 
             (res_target, mrg_target, [('out_file', 'in1')]),
+            (res_target, integrate_1, [('out_file', 'in_file')]),
+            (inputnode, integrate_2, [('in_files', 'in_file')])
+        ])
 
-            (res_tmpl, mrg_tmpl, [('out_file', 'in1')]),
-            (lap_tmpl, norm_lap_tmpl, [('output_image', 'op1')]),
-            (norm_lap_tmpl, mrg_tmpl, [('output_image', 'in2')]),
-
-            #dilation of input mask
-            (inputnode, dil_mask, [('in_mask', 'in_file')]),
-
-            # ants AI inputs
-            (res_tmpl, init_aff, [('out_file', 'fixed_image')]),
-            (res_target, init_aff, [('out_file', 'moving_image')]),
-            (dil_mask, init_aff, [('out_file', 'fixed_image_mask')]),
-
-            # warp mask to individual space
-            (dil_mask, warp_mask, [('out_file', 'input_image')]),
-            (inputnode, warp_mask, [('in_files', 'reference_image')]),
-            (init_aff, warp_mask, [('output_transform', 'transforms')]),
-
-            # normalisation inputs
-            (mrg_tmpl, init_norm, [('out', 'fixed_image')]),
-            (mrg_target, init_norm, [('out', 'moving_image')]),    
-            (dil_mask, init_norm, [('out_file', 'fixed_image_masks')]),
-            (warp_mask, init_norm, [('output_image', 'moving_image_masks')]),
-            (init_aff, init_norm, [('output_transform', 'initial_moving_transform')]),
-
-            #organise normalisation outputs to warp mask
-            (init_norm, split_init_transforms, [('reverse_transforms', 'inlist')]),
-            (split_init_transforms, mrg_init_transforms, [('out2', 'in1')]),
-            (split_init_transforms, mrg_init_transforms, [('out1', 'in2')]),
-
-            (mrg_init_transforms, warp_mask_final, [('out', 'transforms')]),
-            (inputnode, warp_mask_final, [('in_files', 'reference_image')]),
-            (dil_mask, warp_mask_final, [('out_file', 'input_image')]),
-            (warp_mask_final, close_mask, [('output_image', 'in_file')]),
-
-            # mask brains
-            (inu_n4_final, skullstrip_tar, [(('output_image', _pop), 'in_file')]),
-            (close_mask, skullstrip_tar, [('out_file', 'in_mask')]),
-            (inputnode, skullstrip_tpl, [('in_mask', 'in_mask')]),
-
-            # final_normalisation
-            (skullstrip_tpl, final_norm, [('out_file', 'fixed_image')]),
-            (skullstrip_tar, final_norm, [('out_file', 'moving_image')]),
-
-            # Warp mask and labels to subject-space
-            (final_norm, split_final_transforms, [('reverse_transforms', 'inlist')]),
-            (split_final_transforms, mrg_final_transforms, [('out2', 'in1')]),
-            (split_final_transforms, mrg_final_transforms, [('out1', 'in2')]),
-
-            (mrg_final_transforms, warp_seg_mask, [('out', 'transforms')]),
-            (skullstrip_tar, warp_seg_mask, [('out_file', 'reference_image')]),
+    #main workflow
+    wf = pe.Workflow(name)
+    wf.connect([
+        # template prep: dilation of input mask, resampling template, laplacian creation
+        (inputnode, dil_mask, [('in_mask', 'in_file')]),
+        (res_tmpl, mrg_tmpl, [('out_file', 'in1')]),
+        (lap_tmpl, norm_lap_tmpl, [('output_image', 'op1')]),
+        (norm_lap_tmpl, mrg_tmpl, [('output_image', 'in2')]),
+
+        # ants AI inputs
+        (tar_prep, init_aff, [('integrate_1.out_file', 'moving_image')]),
+        (dil_mask, init_aff, [('out_file', 'fixed_image_mask')]),
+        (res_tmpl, init_aff, [('out_file', 'fixed_image')]),
+
+        # warp mask to individual space
+        (dil_mask, warp_mask_1, [('out_file', 'input_image')]),
+        (init_aff, warp_mask_1, [('output_transform', 'transforms')]),
+        (inputnode, warp_mask_1, [('in_files', 'reference_image')]),
+
+        # normalisation inputs
+        (init_aff, init_norm, [('output_transform', 'initial_moving_transform')]),
+        (warp_mask_1, init_norm, [('output_image', 'moving_image_masks')]),
+        (dil_mask, init_norm, [('out_file', 'fixed_image_masks')]),
+        (mrg_tmpl, init_norm, [('out', 'fixed_image')]),
+        (tar_prep, init_norm, [('mrg_target.out', 'moving_image')]),
+
+        #organise initial normalisation transforms for warps
+        (init_norm, split_init_transforms, [('reverse_transforms', 'inlist')]),
+        (split_init_transforms, mrg_init_transforms, [('out2', 'in1')]),
+        (split_init_transforms, mrg_init_transforms, [('out1', 'in2')]),
+
+        # warp mask with initial normalisation transforms
+        (tar_prep, warp_mask_2, [('integrate_2.out_file', 'reference_image')]),
+        (dil_mask, warp_mask_2, [('out_file', 'input_image')]),
+        (mrg_init_transforms, warp_mask_2, [('out', 'transforms')]),
+        (warp_mask_2, close_mask, [('output_image', 'in_file')]),
+
+        # mask brains for refined normalisation
+        (tar_prep, skullstrip_tar, [('integrate_2.out_file', 'in_file')]),
+        (close_mask, skullstrip_tar, [('out_file', 'in_mask')]),
+        (inputnode, skullstrip_tpl, [('in_mask', 'in_mask')]),
+
+        # refined normalisation
+        (skullstrip_tpl, refine_norm, [('out_file', 'fixed_image')]),
+        (skullstrip_tar, refine_norm, [('out_file', 'moving_image')]),
+
+        #organise refined normalisation transforms for warps
+        (refine_norm, split_final_transforms, [('reverse_transforms', 'inlist')]),
+        (split_final_transforms, mrg_final_transforms, [('out2', 'in1')]),
+        (split_final_transforms, mrg_final_transforms, [('out1', 'in2')]),
+
+        #warp mask to subject space and write out
+        (mrg_final_transforms, warp_mask_out, [('out', 'transforms')]),
+        (skullstrip_tar, warp_mask_out, [('out_file', 'reference_image')]),
+        (warp_mask_out, sinker, [('output_image', 'derivatives.@out_mask')]),
+    ])
+    # add second target prep stage if necessary
+    if modality.lower == 't2w':
+        wf.connect([(warp_mask_1, tar_prep, [('output_image', 'inu_n4_final.weight_image')])])
+
+    # add segmentation if necessary
+    if atropos_model:
+        wf.connect([
+            # Warp labels to subject-space
             (mrg_final_transforms, warp_seg_labels, [('out', 'transforms')]),
             (skullstrip_tar, warp_seg_labels, [('out_file', 'reference_image')]),
 
             # Segmentation
             (skullstrip_tar, segment, [('out_file', 'intensity_images')]),
             (warp_seg_labels, segment, [('output_image', 'prior_image')]),
-            (warp_seg_mask, segment, [('output_image', 'mask_image')]),
-            ])
-        return wf
+            (warp_mask_out, segment, [('output_image', 'mask_image')])
+        ])
+
+    return wf
 
 def _pop(in_files):
     if isinstance(in_files, (list, tuple)):
@@ -384,3 +353,8 @@ def res_by_spc(in_file, out_file = None):
 
     nib.save(resampled, out_file)
     return out_file
+
+def _integrate(in_file, out_file = None):
+    import os.path as op     
+    out_file = op.abspath(in_file)
+    return out_file