Merge branch 'enh/antsupdate' of github.com:mgxd/mindboggle into enh/antsupdate

Author: mgxd

install/install_mindboggle.sh

@@ -348,6 +348,10 @@ def first_string_containing_substring(substring, List):
     first_matching_string = [x for x in List if substring in x][0]
     return first_matching_string
 
+# Ensure provenance configuration is inherited by workflow
+if args.prov:
+    config.enable_provenance()
+
 # ============================================================================
 #
 #   Initialize workflow inputs and outputs
@@ -2361,11 +2365,10 @@ if __name__ == '__main__':
     time0 = time()
 
     # ------------------------------------------------------------------------
-    # Workflow configuration: provenance tracking, content hashing, etc.:
+    # Workflow configuration: content hashing, crashfiles, etc.:
     # ------------------------------------------------------------------------
-    if args.prov:
-        config.enable_provenance()
     mbFlow.config['execution']['hash_method'] = 'content'
+    mbFlow.config['execution']['crashfile_format'] = 'txt'
     # mbFlow.config['execution']['use_relative_paths'] = True
 
     # ------------------------------------------------------------------------

mindboggle/mindboggle

@@ -38,7 +38,8 @@ from nipype.interfaces.utility import Merge
 from nipype.interfaces.freesurfer import ReconAll
 from nipype.interfaces.ants.segmentation import CorticalThickness
 from nipype.interfaces.ants import (ApplyTransforms, AntsJointFusion,
-                                    LabelGeometry, Registration)
+                                    LabelGeometry, Registration,
+                                    MultiplyImages)
 from nipype.utils.misc import human_order_sorted
 
 
@@ -108,7 +109,7 @@ def SegOptions(option):
         raise argparse.ArgumentError('ants_seg value must be one of "quick" or "fusion".')
 adv_args.add_argument("--ants_seg", dest="seg",
                       default="quick", type=SegOptions,
-                      help="Use ANTs 'quick' or 'long' to label subcortical structures")
+                      help="Use ANTs 'quick' or 'fusion' to label subcortical structures")
 adv_args.add_argument("--ants_segN", dest="segN", type=int,
                       help="Number of images to use for joint fusion (2-20)")
 adv_args.add_argument("--mb_num_threads", dest="mb_num_threads",
@@ -129,6 +130,10 @@ TDIR = args.template
 if args.skip_freesurfer and args.skip_ants:
     print("Use only one of the skip arguments: --skip_freesurfer, --skip_ants.")
 
+# Ensure provenance configuration is inherited by workflow
+if args.prov:
+    config.enable_provenance()
+
 # ----------------------------------------------------------------------------
 # Initialize workflow inputs and outputs
 # ----------------------------------------------------------------------------
@@ -327,12 +332,13 @@ else:
         # -----------------------------------------------------
         merge_transforms = Node(Merge(2), name="merge_transforms")
         transformer_nn = Node(ApplyTransforms(), name="transformer_nn")
-        transformer_nn.inputs.reference_image = IMAGE
         transformer_nn.inputs.dimension = 3
         transformer_nn.inputs.invert_transform_flags = [False, False]
         transformer_nn.inputs.input_image = '/opt/data/OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_OASIS-30_v2.nii.gz'
         transformer_nn.inputs.interpolation = 'NearestNeighbor'
 
+        mbFlow.connect(corticalthickness, 'BrainSegmentationN4',
+                       transformer_nn, 'reference_image')
         mbFlow.connect(corticalthickness, 'TemplateToSubject1GenericAffine',
                        merge_transforms, 'in1')
         mbFlow.connect(corticalthickness, 'TemplateToSubject0Warp',
@@ -378,22 +384,36 @@ else:
         if args.num_threads and args.num_threads > 1:
             reg.inputs.num_threads = args.num_threads
 
-        transformer_nn = MapNode(ApplyTransforms(), iterfield=['input_image'],
+        transformer_nn = MapNode(ApplyTransforms(),
+                                 iterfield=['input_image', 'transforms'],
                                  name="transformer_nn")
-        transformer_nn.inputs.reference_image = IMAGE
         transformer_nn.inputs.dimension = 3
         transformer_nn.inputs.input_image = labels[:N]
         transformer_nn.inputs.interpolation = 'NearestNeighbor'
 
         labeler = Node(AntsJointFusion(), name='labeler')
         labeler.inputs.dimension = 3
-        labeler.inputs.target_image = [IMAGE]
         labeler.inputs.out_label_fusion = 'label.nii.gz'
         if args.num_threads and args.num_threads > 1:
             labeler.inputs.num_threads = args.num_threads
 
+        def tolist(x):
+            return [x]
+
+        mask_brain = Node(MultiplyImages(dimension=3,
+                                         output_product_image='brain.nii.gz'
+                                         ),
+                          name='mask_brain')
         mbFlow.connect(corticalthickness, 'BrainSegmentationN4',
+                       mask_brain, 'first_input')
+        mbFlow.connect(corticalthickness, 'BrainExtractionMask', mask_brain,
+                       'second_input')
+        mbFlow.connect(mask_brain, 'output_product_image',
                        reg, 'fixed_image')
+        mbFlow.connect(mask_brain, 'output_product_image',
+                       transformer_nn, 'reference_image')
+        mbFlow.connect(mask_brain, ('output_product_image', tolist),
+                       labeler, 'target_image')
         mbFlow.connect(reg, 'composite_transform', transformer_nn, 'transforms')
         mbFlow.connect(corticalthickness, 'BrainExtractionMask', labeler,
                        'mask_image')
@@ -429,10 +449,8 @@ if __name__ == '__main__':
     time0 = time()
 
     # --------------------------------------------------------------------
-    # Workflow configuration: provenance tracking, content hashing, etc.:
+    # Workflow configuration: content hashing, crashfiles, etc.:
     # --------------------------------------------------------------------
-    if args.prov:
-        config.enable_provenance()
     mbFlow.config['execution']['hash_method'] = 'content'
     # mbFlow.config['execution']['use_relative_paths'] = True
     mbFlow.config['execution']['crashfile_format'] = 'txt'