ENH: SimpleITK 2.4.0 migration changes for ObjectMorphology

AutoWorkup/BAW/fsscript.py

@@ -219,7 +219,7 @@ def baw_fixed_brain_mask(
         ## some white matter is being classified as background when it it being avoid due
         ## to too strict of multi-modal thresholding.
         hole_filled = sitk.ErodeObjectMorphology(
-            sitk.DilateObjectMorphology(clipping, fill_size), fill_size
+            sitk.DilateObjectMorphology(clipping, [fill_size] * 3), [fill_size] * 3
         )
         final_mask = sitk.Cast(hole_filled * not_blood, sitk.sitkUInt8)
         ## Now make an mgz version of the binary custom brain mask

AutoWorkup/BAW/utilities/image_processing.py

@@ -38,14 +38,11 @@ def fill_hole_preserved_edge(inputMask, HOLE_FILL_SIZE):
         :return:
         """
 
+        radius = [HOLE_FILL_SIZE] * inputMask.GetDimension()
+        filled_brain_mask = sitk.VotingBinaryHoleFilling(BM, radius)
+        eroded_brain_mask = sitk.ErodeObjectMorphology(filled_brain_mask, radius)
         return sitk.BinaryThreshold(
-            inputMask
-            + sitk.ErodeObjectMorphology(
-                sitk.VotingBinaryHoleFilling(
-                    BM, [HOLE_FILL_SIZE, HOLE_FILL_SIZE, HOLE_FILL_SIZE]
-                ),
-                HOLE_FILL_SIZE,
-            ),
+            inputMask + eroded_brain_mask,
             1,
             10000,
         )

AutoWorkup/BAW/workflows/FixLabelMapsTools.py

@@ -60,14 +60,14 @@ def get_largest_label(inputMask, UseErosionCleaning):
         """
         LargestComponentCode = 1
         if UseErosionCleaning:
-            erosionMask = sitk.ErodeObjectMorphology(inputMask, 1)
+            erosionMask = sitk.ErodeObjectMorphology(inputMask, [1] * 3)
         else:
             erosionMask = inputMask
         CC = sitk.ConnectedComponent(erosionMask)
         Rlabel = sitk.RelabelComponent(CC)
         largestMask = Rlabel == LargestComponentCode
         if UseErosionCleaning:
-            dilateMask = sitk.DilateObjectMorphology(largestMask, 1)
+            dilateMask = sitk.DilateObjectMorphology(largestMask, [1] * 3)
         else:
             dilateMask = largestMask
 

AutoWorkup/BAW/workflows/WorkupAddsonBrainStem.py

@@ -209,13 +209,13 @@ def crop_and_resample_in_place(
         + brainStemBinary * 30
     )
 
-    errod_brain_mask = sitk.ErodeObjectMorphology((outputTissueLabel > 0), 2)
+    errod_brain_mask = sitk.ErodeObjectMorphology((outputTissueLabel > 0), [2] * 3)
     LargestComponentCode = 1
     one_region_mask = (
         sitk.RelabelComponent(sitk.ConnectedComponent(errod_brain_mask))
         == LargestComponentCode
     )
-    dilate_one_region = sitk.DilateObjectMorphology(one_region_mask, 3) > 0
+    dilate_one_region = sitk.DilateObjectMorphology(one_region_mask, [3] * 3) > 0
     cleanedOutputTissueLabel = outputTissueLabel * dilate_one_region
 
     full_output_path = os.path.abspath(ouputTissuelLabelFilename)

AutoWorkup/BAW/workflows/atlasNode.py

@@ -225,12 +225,12 @@ def create_atlas_xml_and_cleaned_deformed_averages(
             print(f"MISSING FILE FROM patternDict: {base_name}")
     ## Make binary dilated mask
     binmask = sitk.BinaryThreshold(load_images_list["AVG_BRAINMASK.nii.gz"], 1, 1000000)
-    brainmask_dilatedBy5 = sitk.DilateObjectMorphology(binmask, 5)
+    brainmask_dilatedBy5 = sitk.DilateObjectMorphology(binmask, [5] * 3)
     brainmask_dilatedBy5 = sitk.Cast(
         brainmask_dilatedBy5, sitk.sitkFloat32
     )  # Convert to Float32 for multiply
 
-    inv_brainmask_erodedBy5 = 1 - sitk.ErodeObjectMorphology(binmask, 5)
+    inv_brainmask_erodedBy5 = 1 - sitk.ErodeObjectMorphology(binmask, [5] * 3)
     inv_brainmask_erodedBy5 = sitk.Cast(
         inv_brainmask_erodedBy5, sitk.sitkFloat32
     )  # Convert to Float32 for multiply

AutoWorkup/BAW/workflows/baseline.py

@@ -123,14 +123,14 @@ def get_largest_label(inputMask, UseErosionCleaning):
         """
         LargestComponentCode = 1
         if UseErosionCleaning:
-            erosionMask = sitk.ErodeObjectMorphology(inputMask, 1)
+            erosionMask = sitk.ErodeObjectMorphology(inputMask, [1] * 3)
         else:
             erosionMask = inputMask
         CC = sitk.ConnectedComponent(erosionMask)
         Rlabel = sitk.RelabelComponent(CC)
         largestMask = Rlabel == LargestComponentCode
         if UseErosionCleaning:
-            dilateMask = sitk.DilateObjectMorphology(largestMask, 1)
+            dilateMask = sitk.DilateObjectMorphology(largestMask, [1] * 3)
         else:
             dilateMask = largestMask
 
@@ -155,10 +155,10 @@ def get_largest_label(inputMask, UseErosionCleaning):
         HDCMARegisteredVentricleLabels == HDMCARightVentricleCode
     )
     ExpandVentValue = 5
-    HDCMAMask_d5 = sitk.DilateObjectMorphology(HDCMAMask, ExpandVentValue)
+    HDCMAMask_d5 = sitk.DilateObjectMorphology(HDCMAMask, [ExpandVentValue] * 3)
     CSFMaskImage = ABCLabelsImage == ABCCSFLabelCode
     VentricleMask = (HDCMAMask_d5 * CSFMaskImage + HDCMAMask) > 0
-    VentricleMask_d2 = sitk.DilateObjectMorphology(VentricleMask, 2)
+    VentricleMask_d2 = sitk.DilateObjectMorphology(VentricleMask, [2] * 3)
     ABCWMLabelCode = 1
     WMMaskImage = ABCLabelsImage == ABCWMLabelCode
 

AutoWorkup/logismosb/interfaces.py

@@ -288,7 +288,8 @@ def fill_lateral_ventricle(
             # Fill the ventricle region
             for i in range(ventricleDilation):
                 ventTemp = (
-                    sitk.DilateObjectMorphology(ventTemp, dilationFactor) * boundary
+                    sitk.DilateObjectMorphology(ventTemp, [dilationFactor] * 3)
+                    * boundary
                 )
                 ventTemp = largest_connected_component(ventTemp)
             return ventTemp
@@ -420,7 +421,7 @@ def create_hemisphere_splits(right_template, left_template):
         # dilate preserved regions
         preservedDilation = 1
         preservedRegions = sitk.DilateObjectMorphology(
-            preservedRegions, preservedDilation
+            preservedRegions, [preservedDilation] * 3
         )
         # add the whtie matter masks to the preserved regions
         preservedRegions = preservedRegions + RightWhiteMatter + LeftWhiteMatter > 0
@@ -434,7 +435,7 @@ def create_hemisphere_splits(right_template, left_template):
         # Remove mask around cerebellum and brain stem
         cerebellumDilation = 1
         cerebellumMaskDilated = sitk.DilateObjectMorphology(
-            CerebellumMask, cerebellumDilation
+            CerebellumMask, [cerebellumDilation] * 3
         )
         leftBoundaryMask = left_hemisphere * (1 - cerebellumMaskDilated)
         leftBoundaryMask = largest_connected_component(leftBoundaryMask)
@@ -449,17 +450,17 @@ def create_hemisphere_splits(right_template, left_template):
             # by eroding the brainlabels mask
             brainlabelserosion = 1
             brainlabelsmask = sitk.ErodeObjectMorphology(
-                brainlabelsImage > 0, brainlabelserosion
+                brainlabelsImage > 0, [brainlabelserosion] * 3
             )
             brainlabelsImage = (
                 sitk.Cast(brainlabelsmask, sitk.sitkUInt32) * brainlabelsImage
             )
         elif boundaryDilation > 0:
             leftBoundaryMask = sitk.DilateObjectMorphology(
-                leftBoundaryMask, boundaryDilation
+                leftBoundaryMask, [boundaryDilation] * 3
             )
             rightBoundaryMask = sitk.DilateObjectMorphology(
-                rightBoundaryMask, boundaryDilation
+                rightBoundaryMask, [boundaryDilation] * 3
             )
 
         # Remove CSF from hemisphere masks

AutoWorkup/logismosb/maclearn/preprocess.py

@@ -239,14 +239,14 @@ def create_water_sheds(aseg_file, filled_file, dilation=6):
     # dilate putamen/pallidum and assign label 12
     rh_pitpul = (
         (
-            sitk.DilateObjectMorphology((fs == 51) + (fs == 52), dilation)
+            sitk.DilateObjectMorphology((fs == 51) + (fs == 52), [dilation] * 3)
             * (rh_hipthal == 0)
         )
         > 0
     ) * wm_labels[2]
     lh_pitpul = (
         (
-            sitk.DilateObjectMorphology((fs == 12) + (fs == 13), dilation)
+            sitk.DilateObjectMorphology((fs == 12) + (fs == 13), [dilation] * 3)
             * (lh_hipthal == 0)
         )
         > 0