diff --git a/AutoWorkup/BRAINSCutCMD.py b/AutoWorkup/BRAINSCutCMD.py
index db50f5ef..a54a93ab 100755
--- a/AutoWorkup/BRAINSCutCMD.py
+++ b/AutoWorkup/BRAINSCutCMD.py
@@ -59,7 +59,11 @@ def xmlGenerator(args, roi=""):
     outputStream.write("          TrainingModelFilename  = \"na\"\n")
     # outputStream.write( "          TrainingModelFilename  = \"/nfsscratch/PREDICT/TEST_BRAINSCut/20120828ANNModel_Model_RF100.txt\"\n")
     outputStream.write("          TestVectorFilename     = \"na\"\n")
-    outputStream.write("          Normalization          = \"" + args.vectorNormalization + "\"\n")
+    if roi == "caudate":
+        outputStream.write("          Normalization          = \"" + 'Linear' + "\"\n")
+    else:
+        outputStream.write("          Normalization          = \"" + args.vectorNormalization + "\"\n")
+
     outputStream.write("   />\n")
 
     #
@@ -125,6 +129,8 @@ def xmlGenerator(args, roi=""):
     if args.inputSubjectT2Filename is not None:
         outputStream.write("    <Image Type=\"T2\" Filename=\"" + args.inputSubjectT2Filename + "\" />\n")
         outputStream.write("    <Image Type=\"GadSG\" Filename=\"" + args.inputSubjectGadSGFilename + "\" />\n")
+    if roi == "caudate":
+        outputStream.write("    <Image Type=\"candiateRegion\" Filename=\"" + args.candidateRegion + "\" />\n")
     # outputStream.write( "    <Image Type=\"TotalGM\" Filename=\"{fn}\" />\n".format(fn=args.inputSubjectTotalGMFilename))
     # outputStream.write( "    <Mask  Type=\"RegistrationROI\" Filename=\"{fn}\" />\n".format(fn=args.inputSubjectRegistrationROIFilename))
 
@@ -163,6 +169,9 @@ def xmlGenerator(args, roi=""):
 
 brainscutParser = argparse.ArgumentParser(description='BRAINSCut command line argument parser')
 
+# HACK:  This is to allow special treatment of caudates with masking
+brainscutParser.add_argument('--candidateRegion', help='Specify the valid candidate region for caudate', required=True)
+
 #
 # input arguments
 #
@@ -232,7 +241,10 @@ def xmlGenerator(args, roi=""):
 
 for roi in roiList:
     currentXmlFilename = xmlGenerator(args, roi)
-    currentModelFilename = args.modelFilename[:-3] + '_' + roi + '.gz'  # trainModelFile.txtD0060NT0060_accumben.gz
+    if roi == "caudate":
+        currentModelFilename = args.modelFilename[:-3] + '_' + roi + '_LinearWithMask.gz'  # trainModelFile.txtD0060NT0060_caudate_LinearWithMask.gz
+    else:
+        currentModelFilename = args.modelFilename[:-3] + '_' + roi + '.gz'  # trainModelFile.txtD0060NT0060_accumben.gz
 
     BRAINSCutCommand = ["BRAINSCut" + " --applyModel " +
                         " --netConfiguration " + currentXmlFilename +
diff --git a/AutoWorkup/PipeLineFunctionHelpers.py b/AutoWorkup/PipeLineFunctionHelpers.py
index 41e031a7..e1ff2ab4 100644
--- a/AutoWorkup/PipeLineFunctionHelpers.py
+++ b/AutoWorkup/PipeLineFunctionHelpers.py
@@ -19,6 +19,27 @@
 def convertToList(element):
     return [ element ]
 
+def MakeInclusionMaskForGMStructures( posteriorDictionary ):
+    import SimpleITK as sitk
+
+    AIR_FN=posteriorDictionary['AIR']
+    CSF_FN=posteriorDictionary['CSF']
+    VB_FN=posteriorDictionary['VB']
+    WM_FN=posteriorDictionary['WM']
+
+    AIR_PROB=sitk.ReadImage(AIR_FN)
+    CSF_PROB=sitk.ReadImage(CSF_FN)
+    VB_PROB=sitk.ReadImage(VB_FN)
+    WM_PROB=sitk.ReadImage(WM_FN)
+
+    AIR_Region=sitk.BinaryThreshold(AIR_PROB,0.51,1.01,1,0)
+    CSF_Region=sitk.BinaryThreshold(CSF_PROB,0.51,1.01,1,0)
+    VB_Region=sitk.BinaryThreshold(VB_PROB,0.51,1.01,1,0)
+    WM_Region=sitk.BinaryThreshold(WM_PROB,0.99,1.01,1,0) #NOTE: Higher tolerance for WM regions!
+
+    outputCandidateRegion=sitk.BinaryThreshold(AIR_Region+CSF_Region+VB_Region+WM_Region,1,100,0,1) #NOTE: Inversion of input/output definitions
+
+    return outputCandidateRegion
 
 def makeListOfValidImages(imageFile):
     if imageFile is None:
diff --git a/AutoWorkup/RF12BRAINSCutWrapper.py b/AutoWorkup/RF12BRAINSCutWrapper.py
index fd7e76f7..86b074f7 100644
--- a/AutoWorkup/RF12BRAINSCutWrapper.py
+++ b/AutoWorkup/RF12BRAINSCutWrapper.py
@@ -21,6 +21,7 @@ class RF12BRAINSCutWrapperCLInputSpec(CommandLineInputSpec):
     ### subject specific
     inputSubjectT1Filename = File(desc="Subject T1 Volume", exists=True, mandatory=True, argstr="--inputSubjectT1Filename %s")
     inputSubjectT2Filename = File(desc="Subject T2 Volume", exists=True, mandatory=False, argstr="--inputSubjectT2Filename %s")
+    candidateRegion = File(desc="Valid region for finding GM structures", exists=True, mandatory=True, argstr="--candidateRegion %s")
     inputSubjectGadSGFilename = File(desc="Subject SG Volume", exists=True, mandatory=False, argstr="--inputSubjectGadSGFilename %s")
     vectorNormalization = traits.Enum("IQR", "Linear", "Sigmoid_Q01", "Sigmoid_Q05", "ZScore", "NONE",
                                       desc="The type of intensity normalization to use", exists=True, mandatory=True, argstr="--vectorNormalization %s")
diff --git a/AutoWorkup/WorkupT1T2.py b/AutoWorkup/WorkupT1T2.py
index ad68a844..b82650fc 100644
--- a/AutoWorkup/WorkupT1T2.py
+++ b/AutoWorkup/WorkupT1T2.py
@@ -774,6 +774,8 @@ def WorkupT1T2(subjectid, mountPrefix, ExperimentBaseDirectoryCache, ExperimentB
                     myLocalSegWF[sessionid] = CreateBRAINSCutWorkflow(projectid, subjectid, sessionid, 'Segmentation',
                                                                       CLUSTER_QUEUE, CLUSTER_QUEUE_LONG, BAtlas[subjectid], t1Only)  # Note:  Passing in the entire BAtlas Object here!
 
+                    baw200.connect(PHASE_2_oneSubjWorkflow[sessionid], 'outputspec.posteriorImages', myLocalSegWF[sessionid],
+                            "inputspec.posteriorDictionary")
                     baw200.connect(PHASE_2_oneSubjWorkflow[sessionid], 'outputspec.t1_average', myLocalSegWF[sessionid], "inputspec.T1Volume")
 
                     if (len(global_AllT2s[sessionid]) > 0):
diff --git a/AutoWorkup/WorkupT1T2BRAINSCut.py b/AutoWorkup/WorkupT1T2BRAINSCut.py
index 442be895..66851c0b 100644
--- a/AutoWorkup/WorkupT1T2BRAINSCut.py
+++ b/AutoWorkup/WorkupT1T2BRAINSCut.py
@@ -126,9 +126,10 @@ def CreateBRAINSCutWorkflow(projectid,
     cutWF = pe.Workflow(name=GenerateWFName(projectid, subjectid, sessionid, WFName))
 
     inputsSpec = pe.Node(interface=IdentityInterface(fields=['T1Volume', 'T2Volume',
-                                                             'TotalGM', 'RegistrationROI',
+                                                             'posteriorImageDictionary', 'RegistrationROI',
                                                              'atlasToSubjectTransform']), name='inputspec')
 
+
     if not t1Only:
         """
         Gradient Anistropic Diffusion images for BRAINSCut
@@ -157,6 +158,7 @@ def CreateBRAINSCutWorkflow(projectid,
         cutWF.connect(GADT1, 'outputVolume', SGI, 'inputVolume1')
         cutWF.connect(GADT2, 'outputVolume', SGI, 'inputVolume2')
 
+
     """
     BRAINSCut
     """
@@ -194,6 +196,14 @@ def CreateBRAINSCutWorkflow(projectid,
     RF12BC.inputs.outputBinaryRightGlobus = 'subjectANNLabel_r_globus.nii.gz'
 
     cutWF.connect(inputsSpec, 'T1Volume', RF12BC, 'inputSubjectT1Filename')
+
+    from PipeLineFunctionHelpers import MakeInclusionMaskForGMStructures;
+    makeCandidateRegionNode = pe.Node(interface=Function(['posteriorDictionary'],
+                                                    ['outputCandidateRegion'],
+                                                    function=MakeInclusionMaskForGMStructures), name="MakeCandidateRegion")
+    cutWF.connect(inputsSpec,'posteriorDictionary',makeCandidateRegionNode,'posteriorDictionary')
+    cutWF.connect(makeCandidateRegionNode,'outputCandidateRegion', RF12BC,'candidateRegion')
+
     if not t1Only:
         cutWF.connect(inputsSpec, 'T2Volume', RF12BC, 'inputSubjectT2Filename')
         # cutWF.connect(inputsSpec,'TotalGM',RF12BC,'inputSubjectTotalGMFilename')