BUG: This version of BRAINSCut is a temporary implementation

The version of BRAINSCut used here is a temporary implementation that
will need to be replaced with a more generic version later.
Currently BRAINSCut takes 30GB of memory, and it is hard-coded
to a specific path.

AutoWorkup/BRAINSTools/BRAINSCutCMD.py

@@ -8,7 +8,7 @@
 import subprocess
 
 def addProbabilityMapElement( probabilityMap, maskName, outputStream ):
-    outputStream.write( "  <ProbabilityMap StructureID    = \""+ maskName + "\" \n")
+    outputStream.write( "  <ProbabilityMap StructureID    = \""+ maskName + "\"\n")
     outputStream.write( "      Gaussian       = \"0.5\"\n")
     outputStream.write( "      GenerateVector = \"true\"\n")
     outputStream.write( "      Filename       = \""+ probabilityMap+"\"\n")
@@ -40,38 +40,39 @@ def xmlGenerator( args ):
     # Registration
     #
     outputStream.write( "  <RegistrationConfiguration \n")
-    outputStream.write( "          ImageTypeToUse  = \"T1\" \n")
-    outputStream.write( "          ID              = \""+registrationID+"\" \n")
-    outputStream.write( "          BRAINSROIAutoDilateSize= \"1\" \n")
+    outputStream.write( "          ImageTypeToUse  = \"T1\"\n")
+    outputStream.write( "          ID              = \""+registrationID+"\"\n")
+    outputStream.write( "          BRAINSROIAutoDilateSize= \"1\"\n")
     outputStream.write( "   />\n")
 
     #
     # training vector configuration  (feature vector)
     #
 
-    outputStream.write( "   <NeuralNetParams MaskSmoothingValue     = \"0.0\" \n")
+    outputStream.write( "   <NeuralNetParams MaskSmoothingValue     = \"0.0\"\n")
     outputStream.write( "          GradientProfileSize    = \"1\"\n")
-    outputStream.write( "          TrainingVectorFilename = \""+args.trainingVectorFilename+"\" \n")
-    outputStream.write( "          TrainingModelFilename  = \""+args.modelFileBasename+"\" \n")
-    outputStream.write( "          TestVectorFilename     = \"na\" \n")
-    outputStream.write( "          Normalization          = \""+args.vectorNormalization+"\" \n")
+    outputStream.write( "          TrainingVectorFilename = \""+args.trainingVectorFilename+"\"\n")
+    # outputStream.write( "          TrainingModelFilename  = \""+args.modelFileBasename+"\"\n")
+    outputStream.write( "          TrainingModelFilename  = \"/nfsscratch/PREDICT/TEST_BRAINSCut/20120828ANNModel_Model_RF100.txt\"\n")
+    outputStream.write( "          TestVectorFilename     = \"na\"\n")
+    outputStream.write( "          Normalization          = \""+args.vectorNormalization+"\"\n")
     outputStream.write( "   />\n")
 
     #
     # random forest parameters
     #
     outputStream.write( "   <RandomForestParameters \n")
-    outputStream.write( "      MaxDepth= \"100\" \n")     #dummy
-    outputStream.write( "      MaxTreeCount= \"100\" \n") # dummy
+    outputStream.write( "      MaxDepth= \"100\"\n")     #dummy
+    outputStream.write( "      MaxTreeCount= \"100\"\n") # dummy
     outputStream.write( "      MinSampleCount= \"5\"\n")
-    outputStream.write( "      UseSurrogates= \"false\" \n")
+    outputStream.write( "      UseSurrogates= \"false\"\n")
     outputStream.write( "      CalcVarImportance= \"false\"\n")
     outputStream.write( "      />\n")
 
     #
     # ANN Parameters
     #
-    outputStream.write( "   <ANNParameters Iterations             = \"5\" \n")
+    outputStream.write( "   <ANNParameters Iterations             = \"5\"\n")
     outputStream.write( "                     MaximumVectorsPerEpoch = \"70000\"\n")
     outputStream.write( "                     EpochIterations        = \"100\"\n")
     outputStream.write( "                     ErrorInterval          = \"1\"\n")
@@ -85,8 +86,8 @@ def xmlGenerator( args ):
     # apply conditions
     #
     outputStream.write( "<ApplyModel         CutOutThresh           = \"0.05\"\n")
-    outputStream.write( "                    MaskThresh             = \"0.5\" \n")
-    outputStream.write( "                    GaussianSmoothingSigma = \"0.0\" \n")
+    outputStream.write( "                    MaskThresh             = \"0.5\"\n")
+    outputStream.write( "                    GaussianSmoothingSigma = \"0.0\"\n")
     outputStream.write( "   />\n")
 
     #
@@ -129,7 +130,7 @@ def xmlGenerator( args ):
         outputStream.write( '    <Registration SubjToAtlasRegistrationFilename="'+args.deformationFromSubjectToTemplate+'"\n')
     else:
         outputStream.write( '    <Registration SubjToAtlasRegistrationFilename="" \n')
-    outputStream.write( "       AtlasToSubjRegistrationFilename=\""+args.deformationFromTemplateToSubject+"\" \n")
+    outputStream.write( "       AtlasToSubjRegistrationFilename=\""+args.deformationFromTemplateToSubject+"\"\n")
     outputStream.write( "       ID=\""+registrationID+"\" /> \n")
     outputStream.write( "  </DataSet>\n")
 
@@ -212,14 +213,15 @@ def xmlGenerator( args ):
 else:
     print("no xml filename is given to process")
 
+#                 " --modelFilename " + args.modelFilename +
 
-subprocess.call(["BRAINSCut" +
-                 " --applyModel " +
+BRAINSCutCommand=["BRAINSCut" + " --applyModel " +
                  " --netConfiguration " + args.xmlFilename +
-                 " --modelFilename " + args.modelFilename +
-                 " --method RandomForest"
-                 ],
-                 shell=True)
+                 " --method RandomForest" +
+                 " --numberOfTrees 100  --randomTreeDepth 100"
+                 ]
+print("HACK:  BRAINCUT COMMAND: {0}".format(BRAINSCutCommand))
+subprocess.call(BRAINSCutCommand, shell=True)
 """
 script to be run
   /ipldev/scratch/eunyokim/src/BRAINS20111028/build-Darwin-Debug/lib/BRAINSCut  --applyModel --netConfiguration /ipldev/scratch/eunyokim/src/BRAINS20111028/build-Darwin/BRAINSTools-build/BRAINSCut/TestSuite/TestSuite/NetConfigurations/output.xml --modelFilename /hjohnson/HDNI/PREDICT_TRAINING/regina_ann/TrainingModels/BRAINSAutoWorkUpTest/GadSG/Test9/TrainedModels/20110919ANNModel_allSubcorticals.txtD0050NT0050   --method RandomForest

AutoWorkup/BRAINSTools/RF8BRAINSCutWrapper.py

@@ -73,7 +73,7 @@ class RF8BRAINSCutWrapperCLOutputSpec(TraitedSpec):
     outputBinaryRightPutamen = File( desc = "Output binary file of right putamen", exists = True, mandatory = True)
 
     outputBinaryLeftThalamus = File( desc = "Output binary file of left thalamus", exists = True, mandatory = True)
-    outputBinaryRightThalamus = File( desc = "Output binary file:0 of right thalamus", exists = True, mandatory = True)
+    outputBinaryRightThalamus = File( desc = "Output binary file of right thalamus", exists = True, mandatory = True)
 
 class RF8BRAINSCutWrapper(CommandLine):
     """

AutoWorkup/WorkupT1T2.py

@@ -334,13 +334,19 @@ def WorkupT1T2(subjectid,mountPrefix,ExperimentBaseDirectoryCache, ExperimentBas
         allSessions = ExperimentDatabase.getSessionsFromSubject(subjectid)
         print("Running sessions: {ses} for subject {sub}".format(ses=allSessions,sub=subjectid))
         BAtlas[subjectid] = MakeAtlasNode(atlas_fname_wpath,"BAtlas_"+str(subjectid)) ## Call function to create node
+
+
         for sessionid in allSessions:
             global_AllT1s=ExperimentDatabase.getFilenamesByScantype(sessionid,['T1-30','T1-15'])
             global_AllT2s=ExperimentDatabase.getFilenamesByScantype(sessionid,['T2-30','T2-15'])
             global_AllPDs=ExperimentDatabase.getFilenamesByScantype(sessionid,['PD-30','PD-15'])
             global_AllFLs=ExperimentDatabase.getFilenamesByScantype(sessionid,['FL-30','FL-15'])
             global_AllOthers=ExperimentDatabase.getFilenamesByScantype(sessionid,['OTHER-30','OTHER-15'])
-            #print("HACK:  all FLs: {0}".format(ExperimentDatabase.getFilenamesByScantype(sessionid,['FL-30','FL-15'])))
+            print("HACK:  all T1s: {0} {1}".format(global_AllT1s, len(global_AllT1s) ))
+            print("HACK:  all T2s: {0} {1}".format(global_AllT2s, len(global_AllT2s) ))
+            print("HACK:  all PDs: {0} {1}".format(global_AllPDs, len(global_AllPDs) ))
+            print("HACK:  all FLs: {0} {1}".format(global_AllFLs, len(global_AllFLs) ))
+            print("HACK:  all Others: {0} {1}".format(global_AllOthers, len(global_AllOthers) ))
 
             projectid = ExperimentDatabase.getProjFromSession(sessionid)
             print("PROJECT: {0} SUBJECT: {1} SESSION: {2}".format(projectid,subjectid,sessionid))
@@ -598,7 +604,22 @@ def WorkupT1T2(subjectid,mountPrefix,ExperimentBaseDirectoryCache, ExperimentBas
                     baw200.connect(ClipT1ImageWithBrainMaskNode[sessionid], 'clipped_file', AtlasToSubjectantsRegistration[subjectid], 'fixed_image')
                     baw200.connect(PHASE_2_oneSubjWorkflow[sessionid],'OutputSpec.atlasToSubjectTransform',AtlasToSubjectantsRegistration[subjectid],'initial_moving_transform')
 
-                if 'SEGMENTATION' in WORKFLOW_COMPONENTS and len(global_AllT2s) > 0: # Currently only works with multi-modal_data
+                global_AllT1s=ExperimentDatabase.getFilenamesByScantype(sessionid,['T1-30','T1-15'])
+                global_AllT2s=ExperimentDatabase.getFilenamesByScantype(sessionid,['T2-30','T2-15'])
+                global_AllPDs=ExperimentDatabase.getFilenamesByScantype(sessionid,['PD-30','PD-15'])
+                global_AllFLs=ExperimentDatabase.getFilenamesByScantype(sessionid,['FL-30','FL-15'])
+                global_AllOthers=ExperimentDatabase.getFilenamesByScantype(sessionid,['OTHER-30','OTHER-15'])
+                print("HACK2:  all T1s: {0} {1}".format(global_AllT1s, len(global_AllT1s) ))
+                print("HACK2:  all T2s: {0} {1}".format(global_AllT2s, len(global_AllT2s) ))
+                print("HACK2:  all PDs: {0} {1}".format(global_AllPDs, len(global_AllPDs) ))
+                print("HACK2:  all FLs: {0} {1}".format(global_AllFLs, len(global_AllFLs) ))
+                print("HACK2:  all Others: {0} {1}".format(global_AllOthers, len(global_AllOthers) ))
+                if ( 'SEGMENTATION' in WORKFLOW_COMPONENTS ) : # Currently only works with multi-modal_data
+                     print("HACK SEGMENTATION IN  WORKFLOW_COMPONENTS")
+                if ( len(global_AllT2s) > 0 ): # Currently only works with multi-modal_data
+                    print("HACK len(global_AllT2s) > 0 ")
+                print("HACK")
+                if ( 'SEGMENTATION' in WORKFLOW_COMPONENTS ) and ( len(global_AllT2s) > 0 ): # Currently only works with multi-modal_data
                     from WorkupT1T2BRAINSCut import CreateBRAINSCutWorkflow
                     myLocalSegWF[subjectid] = CreateBRAINSCutWorkflow(projectid, subjectid, sessionid,'Segmentation',CLUSTER_QUEUE,BAtlas[subjectid]) ##Note:  Passing in the entire BAtlas Object here!
                     baw200.connect( PHASE_2_oneSubjWorkflow[sessionid], 'OutputSpec.t1_average', myLocalSegWF[subjectid], "InputSpec.T1Volume" )
@@ -625,6 +646,8 @@ def WorkupT1T2(subjectid,mountPrefix,ExperimentBaseDirectoryCache, ExperimentBas
                     baw200.connect(myLocalSegWF[subjectid], 'OutputSpec.outputBinaryRightThalamus',SEGMENTATION_DataSink[subjectid], 'BRAINSCut.@outputBinaryRightThalamus')
                     baw200.connect(myLocalSegWF[subjectid], 'OutputSpec.outputLabelImageName', SEGMENTATION_DataSink[subjectid],'BRAINSCut.@outputLabelImageName')
                     baw200.connect(myLocalSegWF[subjectid], 'OutputSpec.outputCSVFileName', SEGMENTATION_DataSink[subjectid],'BRAINSCut.@outputCSVFileName')
+                else:
+                    print("SKIPPING SEGMENTATION PHASE FOR {0} {1} {2}, lenT2s {3}".format(projectid, subjectid, sessionid, len(global_AllT2s) ))
 
                 if 'FREESURFER' in WORKFLOW_COMPONENTS and len(global_AllT2s) > 0: # Currently only works with multi-modal_data
                     RunAllFSComponents=True ## A hack to avoid 26 hour run of freesurfer

AutoWorkup/WorkupT1T2BRAINSCut.py

@@ -17,36 +17,48 @@ def CreateLabelMap(listOfImages,LabelImageName,CSVFileName):
     A function to create a consolidated label map and a
     csv file of volume measurements.
     """
+
+    """
+    subjectANNLabel_l_caudate.nii.gz
+    subjectANNLabel_l_hippocampus.nii.gz
+    subjectANNLabel_l_putamen.nii.gz
+    subjectANNLabel_l_thalamus.nii.gz
+    subjectANNLabel_r_caudate.nii.gz
+    subjectANNLabel_r_hippocampus.nii.gz
+    subjectANNLabel_r_putamen.nii.gz
+    subjectANNLabel_r_thalamus.nii.gz
+    """
     import SimpleITK as sitk
     import os
     import csv
     orderOfPriority = [
-      "l_Caudate_seg"     ,
-      "r_Caudate_seg"     ,
-      "l_Putamen_seg"     ,
-      "r_Putamen_seg"     ,
-      "l_Hippocampus_seg" ,
-      "r_Hippocampus_seg" ,
-      "l_Thalamus_seg"    ,
-      "r_Thalamus_seg"
+      "l_caudate"     ,
+      "r_caudate"     ,
+      "l_putamen"     ,
+      "r_putamen"     ,
+      "l_hippocampus" ,
+      "r_hippocampus" ,
+      "l_thalamus"    ,
+      "r_thalamus"
     ]
 
     valueDict={
-        "l_Caudate_seg"     : 1,
-        "r_Caudate_seg"     : 2,
-        "l_Putamen_seg"     : 3,
-        "r_Putamen_seg"     : 4,
-        "l_Hippocampus_seg" : 5,
-        "r_Hippocampus_seg" : 6,
-        "l_Thalamus_seg"    : 7,
-        "r_Thalamus_seg"    : 8
+        "l_caudate"     : 1,
+        "r_caudate"     : 2,
+        "l_putamen"     : 3,
+        "r_putamen"     : 4,
+        "l_hippocampus" : 5,
+        "r_hippocampus" : 6,
+        "l_thalamus"    : 7,
+        "r_thalamus"    : 8
     }
 
     labelImage = None
     for segFN in listOfImages:
         im = sitk.ReadImage(segFN)
         im.GetSize()
-        structName=os.path.basename(segFN.replace(".nii.gz",""))
+        remove_pre_postfix=os.path.basename(segFN.replace(".nii.gz","").replace("subjectANNLabel_","").replace("_seg",""))
+        structName=remove_pre_postfix.lower()
         if labelImage is None:
             labelImage = im*valueDict[structName]
         else:
@@ -139,14 +151,25 @@ def CreateBRAINSCutWorkflow(projectid, subjectid, sessionid,WFName,CLUSTER_QUEUE
     RF8BC.inputs.trainingVectorFilename = "trainingVectorFilename.txt"
     RF8BC.inputs.xmlFilename = "BRAINSCutSegmentationDefinition.xml"
 
-    RF8BC.inputs.outputBinaryLeftCaudate=      'l_Caudate_seg.nii.gz'
-    RF8BC.inputs.outputBinaryRightCaudate=     'r_Caudate_seg.nii.gz'
-    RF8BC.inputs.outputBinaryLeftHippocampus=  'l_Hippocampus_seg.nii.gz'
-    RF8BC.inputs.outputBinaryRightHippocampus= 'r_Hippocampus_seg.nii.gz'
-    RF8BC.inputs.outputBinaryLeftPutamen=      'l_Putamen_seg.nii.gz'
-    RF8BC.inputs.outputBinaryRightPutamen=     'r_Putamen_seg.nii.gz'
-    RF8BC.inputs.outputBinaryLeftThalamus=     'l_Thalamus_seg.nii.gz'
-    RF8BC.inputs.outputBinaryRightThalamus=    'r_Thalamus_seg.nii.gz'
+    """ HACK These should be l_Caudate_seg.nii.gz
+    subjectANNLabel_l_caudate.nii.gz
+    subjectANNLabel_l_hippocampus.nii.gz
+    subjectANNLabel_l_putamen.nii.gz
+    subjectANNLabel_l_thalamus.nii.gz
+    subjectANNLabel_r_caudate.nii.gz
+    subjectANNLabel_r_hippocampus.nii.gz
+    subjectANNLabel_r_putamen.nii.gz
+    subjectANNLabel_r_thalamus.nii.gz
+    """
+
+    RF8BC.inputs.outputBinaryLeftCaudate=      'subjectANNLabel_l_caudate.nii.gz'
+    RF8BC.inputs.outputBinaryRightCaudate=     'subjectANNLabel_r_caudate.nii.gz'
+    RF8BC.inputs.outputBinaryLeftHippocampus=  'subjectANNLabel_l_hippocampus.nii.gz'
+    RF8BC.inputs.outputBinaryRightHippocampus= 'subjectANNLabel_r_hippocampus.nii.gz'
+    RF8BC.inputs.outputBinaryLeftPutamen=      'subjectANNLabel_l_putamen.nii.gz'
+    RF8BC.inputs.outputBinaryRightPutamen=     'subjectANNLabel_r_putamen.nii.gz'
+    RF8BC.inputs.outputBinaryLeftThalamus=     'subjectANNLabel_l_thalamus.nii.gz'
+    RF8BC.inputs.outputBinaryRightThalamus=    'subjectANNLabel_r_thalamus.nii.gz'
 
     cutWF.connect(inputsSpec,'T1Volume',RF8BC,'inputSubjectT1Filename')
     cutWF.connect(inputsSpec,'T2Volume',RF8BC,'inputSubjectT2Filename')
@@ -171,6 +194,8 @@ def CreateBRAINSCutWorkflow(projectid, subjectid, sessionid,WFName,CLUSTER_QUEUE
     cutWF.connect(atlasObject,'r_thalamus_ProbabilityMap',RF8BC,'probabilityMapsRightThalamus')
     ##TODO:
     cutWF.connect(atlasObject,'RandomForestAllSubcorticalsBalancedModel_txtD0060NT0060_gz',RF8BC,'modelFilename')
+    ##HACK: Needs to be fixed
+    #RF8BC.inputs.modelFilename='/nfsscratch/PREDICT/TEST_BRAINSCut/20120828ANNModel_Model_RF100.txt'
 
     cutWF.connect(inputsSpec,'atlasToSubjectTransform',RF8BC,'deformationFromTemplateToSubject')