Adding BIANCA processing

bip/pipelines/struct_FS/FS_proc.py

@@ -34,8 +34,8 @@ def run(ctx,
 
         env = dict(os.environ, SUBJECTS_DIR=os.getcwd() + "/" + ctx.subject)
 
-        T1_path = subjects_dir + "/" + T1_unbiased
-        T2_path = subjects_dir + "/" + T2_FLAIR_unbiased
+        T1_path = os.getcwd() + "/" + T1_unbiased
+        T2_path = os.getcwd() + "/" + T2_FLAIR_unbiased
 
         cmd = 'recon-all -all -s FreeSurfer -i ' + T1_path
 

bip/pipelines/struct_T2_FLAIR/T2_FLAIR_bianca.py

+#!/usr/bin/env python
+#
+# T2_FLAIR_bianca.py -  Sub-pipeline with FSL's BIANCA processing of T2 FLAIR.
+#
+# Author: Gaurav Bhalerao <gaurav.bhalerao@psych.ox.ac.uk>
+# Author: Fidel Alfaro Almagro <fidel.alfaroalmagro@ndcn.ox.ac.uk>
+#
+# pylint: disable=C0103,E0602,C0114,C0115,C0116,R0913,R0914,R0915
+# pylint: disable=W0613,C0301
+#
+
+import logging
+from shutil import copyfile
+from pipe_tree import In, Out, Ref
+from fsl import wrappers
+from bip.utils.log_utils import redirect_logging
+
+log = logging.getLogger(__name__)
+
+def run(ctx,
+        T1_unbiased:                           In,
+        T2_FLAIR_unbiased:                     In,
+        T1_to_MNI_warp_coef_inv:               In,
+        T1_to_MNI_linear_mat:                  In,
+        T1_fast_pve_0:                         In,
+        T1_unbiased_brain:                     In,
+        T1_brain_mask:                         In,
+        logs_dir:                              Ref,
+        T1_dir:                                Ref,
+        T2_FLAIR_dir:                          Ref,
+        T2_FLAIR_bianca_lesion_dir:            Ref,
+        T1_transforms_dir:                     Ref,
+        T1_unbiased_brain_mask:                Out,
+        T1_unbiased_bianca_mask:               Out,
+        T1_unbiased_ventmask:                  Out,
+        bianca_T1_unbiased_brain_mask:         Out,
+        bianca_T1_unbiased_bianca_mask:        Out,
+        bianca_T1_unbiased_ventmask:           Out,
+        T2_FLAIR_bianca_conf_file:             Out,
+        T2_FLAIR_bianca_mask:                  Out,
+        T2_FLAIR_bianca_final_mask:            Out,
+        T2_FLAIR_bianca_deepwm_map:            Out,
+        T2_FLAIR_bianca_perivent_map:          Out,
+        T2_FLAIR_bianca_tot_pvent_deep_volume: Out,
+        T2_FLAIR_bianca_volume:                Out):
+
+    with redirect_logging('T2_FLAIR_bianca', outdir=logs_dir):
+
+        # Create bianca mask
+        wrappers.make_bianca_mask(T1_unbiased, T1_fast_pve_0,
+                                  T1_to_MNI_warp_coef_inv, keep_files=False)
+
+        copyfile(src=T1_unbiased_brain_mask,dst=bianca_T1_unbiased_brain_mask)
+        copyfile(src=T1_unbiased_bianca_mask,dst=bianca_T1_unbiased_bianca_mask)
+        copyfile(src=T1_unbiased_ventmask,dst=bianca_T1_unbiased_ventmask)
+
+        # Create masterfile for bianca
+        filenames =[T1_unbiased_brain, T2_FLAIR_unbiased, T1_to_MNI_linear_mat]
+        with open(T2_FLAIR_bianca_conf_file, 'w', encoding="utf-8") as f:
+            for j in filenames:
+                f.write(j+" ")
+
+        # Run bianca
+        bianca_class_data = ctx.get_data("bianca/bianca_class_data")
+        wrappers.bianca(singlefile = T2_FLAIR_bianca_conf_file,
+                        querysubjectnum = 1, brainmaskfeaturenum = 1,
+                        loadclassifierdata = bianca_class_data,
+                        matfeaturenum = 3, featuresubset = "1,2",
+                        o = T2_FLAIR_bianca_mask)
+
+        # Multiply the lesions mask (bianca_mask) by the "unbiased_bianca_mask"
+        wrappers.fslmaths(T2_FLAIR_bianca_mask).mul(T1_brain_mask).thr(0.8).bin().run(T2_FLAIR_bianca_final_mask)
+
+        # Calculate total WMH (volume of the final mask),
+        # periventricular WMH & deep WMH - 10mm Criteria
+        wrappers.bianca_perivent_deep(wmh_map = T2_FLAIR_bianca_final_mask,
+                                      vent_mask = bianca_T1_unbiased_ventmask,
+                                      minclustersize = 0,
+                                      outputdir = T2_FLAIR_bianca_lesion_dir,
+                                      do_stats = 2)
+
+        vals = wrappers.fslstats(T2_FLAIR_bianca_final_mask).V.run()[0]
+        with open(T2_FLAIR_bianca_volume, 'wt', encoding="utf-8") as f:
+            f.write(str(vals) + "\n")

bip/pipelines/struct_T2_FLAIR/struct_T2_FLAIR.py

@@ -13,6 +13,7 @@ import logging
 from bip.utils.log_utils           import redirect_logging
 from bip.pipelines.struct_T2_FLAIR import T2_FLAIR_gdc, T2_FLAIR_brain_extract
 from bip.pipelines.struct_T2_FLAIR import T2_FLAIR_defacing, T2_FLAIR_apply_bfc
+from bip.pipelines.struct_T2_FLAIR import T2_FLAIR_bianca
 
 log = logging.getLogger(__name__)
 
@@ -39,5 +40,10 @@ def add_to_pipeline(ctx, pipe, tree, targets):
              submit=dict(jobtime=200, name="BIP_T2_FLAIR_apply_bfc_" + subj),
              kwargs={'ctx' : ctx})
         targets.append('T2_FLAIR_unbiased_brain')
+        pipe(T2_FLAIR_bianca.run,
+             submit=dict(jobtime=200, name="BIP_T2_FLAIR_bianca_" + subj),
+             kwargs={'ctx' : ctx})
+        targets.append('T2_FLAIR_bianca_volume')
+
 
     return pipe, targets