def runWatershed(self, masterImageData, seedLabelmap, outputLabelmap):
masterVolumeNode = slicer.vtkMRMLScalarVolumeNode()
slicer.mrmlScene.AddNode(masterVolumeNode)
slicer.vtkSlicerSegmentationsModuleLogic.CopyOrientedImageDataToVolumeNode(masterImageData, masterVolumeNode)
seedLabelmapNode = slicer.vtkMRMLLabelMapVolumeNode()
slicer.mrmlScene.AddNode(seedLabelmapNode)
slicer.vtkSlicerSegmentationsModuleLogic.CopyOrientedImageDataToVolumeNode(seedLabelmap, seedLabelmapNode)
# Read input data from Slicer into SimpleITK
labelImage = sitk.ReadImage(sitkUtils.GetSlicerITKReadWriteAddress(seedLabelmapNode.GetName()))
backgroundImage = sitk.ReadImage(sitkUtils.GetSlicerITKReadWriteAddress(masterVolumeNode.GetName()))
# Run watershed filter
featureImage = sitk.GradientMagnitudeRecursiveGaussian(backgroundImage, float(self.scriptedEffect.doubleParameter(FEATURE_SIZE_MM_PARAMETER_NAME)))
del backgroundImage
f = sitk.MorphologicalWatershedFromMarkersImageFilter()
f.SetMarkWatershedLine(False)
f.SetFullyConnected(False)
labelImage = f.Execute(featureImage, labelImage)
del featureImage
# Pixel type of watershed output is the same as the input. Convert it to int16 now.
if labelImage.GetPixelID() != sitk.sitkInt16:
labelImage = sitk.Cast(labelImage, sitk.sitkInt16)
# Write result from SimpleITK to Slicer. This currently performs a deep copy of the bulk data.
sitk.WriteImage(labelImage, sitkUtils.GetSlicerITKReadWriteAddress(seedLabelmapNode.GetName()))
# Update segmentation from labelmap node and remove temporary nodes
outputLabelmap.ShallowCopy(seedLabelmapNode.GetImageData())
outputLabelmap.SetExtent(masterImageData.GetExtent())
slicer.mrmlScene.RemoveNode(masterVolumeNode)
slicer.mrmlScene.RemoveNode(seedLabelmapNode)
def apply(self):
if not self.editUtil.getBackgroundImage() or not self.editUtil.getLabelImage():
return
node = self.editUtil.getParameterNode()
self.undoRedo.saveState()
#
# get the label and background images
#
sliceLogic = self.sliceWidget.sliceLogic()
labelLogic = sliceLogic.GetLabelLayer()
labelNode = labelLogic.GetVolumeNode()
labelNodeName = labelNode.GetName()
backgroundLogic = sliceLogic.GetBackgroundLayer()
backgroundNode = backgroundLogic.GetVolumeNode()
backgroundNodeName = backgroundNode.GetName()
backgroundImage = sitk.ReadImage( sitkUtils.GetSlicerITKReadWriteAddress( backgroundNodeName ) )
filter = sitk.DoubleThresholdImageFilter()
filter.SetThreshold1( self.outer_min )
filter.SetThreshold2( self.min )
filter.SetThreshold3( self.max )
filter.SetThreshold4( self.outer_max )
filter.FullyConnectedOn()
filter.SetInsideValue( self.editUtil.getLabel() )
filter.SetOutsideValue( 0 )
# todo
# cast to output scalar type
sitk.WriteImage( filter.Execute(backgroundImage), sitkUtils.GetSlicerITKReadWriteAddress( labelNodeName ) )
def getBoundingBox(self, fixedLabelNodeID, movingLabelNodeID):
ls = sitk.LabelStatisticsImageFilter()
fixedLabelNode = slicer.mrmlScene.GetNodeByID(fixedLabelNodeID)
movingLabelNode = slicer.mrmlScene.GetNodeByID(movingLabelNodeID)
fixedLabelAddress = sitkUtils.GetSlicerITKReadWriteAddress(
fixedLabelNode.GetName())
movingLabelAddress = sitkUtils.GetSlicerITKReadWriteAddress(
movingLabelNode.GetName())
fixedLabelImage = sitk.ReadImage(fixedLabelAddress)
movingLabelImage = sitk.ReadImage(movingLabelAddress)
cast = sitk.CastImageFilter()
cast.SetOutputPixelType(2)
unionLabelImage = (cast.Execute(fixedLabelImage) +
cast.Execute(movingLabelImage)) > 0
unionLabelImage = cast.Execute(unionLabelImage)
ls.Execute(unionLabelImage, unionLabelImage)
bb = ls.GetBoundingBox(1)
print(str(bb))
size = unionLabelImage.GetSize()
bbMin = (max(0, bb[0] - 30), max(0, bb[2] - 30), max(0, bb[4] - 5))
bbMax = (size[0] - min(size[0], bb[1] + 30),
size[1] - min(size[1], bb[3] + 30),
size[2] - (min(size[2], bb[5] + 5)))
return (bbMin, bbMax)
def computePreviewLabelmap(self, mergedImage, outputLabelmap):
import vtkSegmentationCorePython as vtkSegmentationCore
import vtkSlicerSegmentationsModuleLogicPython as vtkSlicerSegmentationsModuleLogic
# This can be a long operation - indicate it to the user
qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
masterVolumeNode = slicer.vtkMRMLScalarVolumeNode()
slicer.mrmlScene.AddNode(masterVolumeNode)
slicer.vtkSlicerSegmentationsModuleLogic.CopyOrientedImageDataToVolumeNode(
self.clippedMasterImageData, masterVolumeNode)
mergedLabelmapNode = slicer.vtkMRMLLabelMapVolumeNode()
slicer.mrmlScene.AddNode(mergedLabelmapNode)
slicer.vtkSlicerSegmentationsModuleLogic.CopyOrientedImageDataToVolumeNode(
mergedImage, mergedLabelmapNode)
outputRasToIjk = vtk.vtkMatrix4x4()
mergedImage.GetImageToWorldMatrix(outputRasToIjk)
outputExtent = mergedImage.GetExtent()
# Run segmentation algorithm
import SimpleITK as sitk
import sitkUtils
# Read input data from Slicer into SimpleITK
labelImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(
mergedLabelmapNode.GetName()))
backgroundImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(masterVolumeNode.GetName()))
# Run watershed filter
featureImage = sitk.GradientMagnitudeRecursiveGaussian(
backgroundImage,
float(self.scriptedEffect.doubleParameter("ObjectScaleMm")))
del backgroundImage
f = sitk.MorphologicalWatershedFromMarkersImageFilter()
f.SetMarkWatershedLine(False)
f.SetFullyConnected(False)
labelImage = f.Execute(featureImage, labelImage)
del featureImage
# Pixel type of watershed output is the same as the input. Convert it to int16 now.
if labelImage.GetPixelID() != sitk.sitkInt16:
labelImage = sitk.Cast(labelImage, sitk.sitkInt16)
# Write result from SimpleITK to Slicer. This currently performs a deep copy of the bulk data.
sitk.WriteImage(
labelImage,
sitkUtils.GetSlicerITKReadWriteAddress(
mergedLabelmapNode.GetName()))
# Update segmentation from labelmap node and remove temporary nodes
outputLabelmap.ShallowCopy(mergedLabelmapNode.GetImageData())
outputLabelmap.SetImageToWorldMatrix(outputRasToIjk)
outputLabelmap.SetExtent(outputExtent)
slicer.mrmlScene.RemoveNode(masterVolumeNode)
slicer.mrmlScene.RemoveNode(mergedLabelmapNode)
qt.QApplication.restoreOverrideCursor()
def onApply(self):
# Get list of visible segment IDs, as the effect ignores hidden segments.
segmentationNode = self.scriptedEffect.parameterSetNode().GetSegmentationNode()
visibleSegmentIds = vtk.vtkStringArray()
segmentationNode.GetDisplayNode().GetVisibleSegmentIDs(visibleSegmentIds)
if visibleSegmentIds.GetNumberOfValues() == 0:
logging.info("Smoothing operation skipped: there are no visible segments")
return
# This can be a long operation - indicate it to the user
qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
# Allow users revert to this state by clicking Undo
self.scriptedEffect.saveStateForUndo()
# Export master image data to temporary new volume node.
# Note: Although the original master volume node is already in the scene, we do not use it here,
# because the master volume may have been resampled to match segmentation geometry.
import vtkSegmentationCorePython as vtkSegmentationCore
masterVolumeNode = slicer.vtkMRMLScalarVolumeNode()
slicer.mrmlScene.AddNode(masterVolumeNode)
masterVolumeNode.SetAndObserveTransformNodeID(segmentationNode.GetTransformNodeID())
slicer.vtkSlicerSegmentationsModuleLogic.CopyOrientedImageDataToVolumeNode(self.scriptedEffect.masterVolumeImageData(), masterVolumeNode)
# Generate merged labelmap of all visible segments, as the filter expects a single labelmap with all the labels.
mergedLabelmapNode = slicer.vtkMRMLLabelMapVolumeNode()
slicer.mrmlScene.AddNode(mergedLabelmapNode)
slicer.vtkSlicerSegmentationsModuleLogic.ExportSegmentsToLabelmapNode(segmentationNode, visibleSegmentIds, mergedLabelmapNode, masterVolumeNode)
# Run segmentation algorithm
import SimpleITK as sitk
import sitkUtils
# Read input data from Slicer into SimpleITK
labelImage = sitk.ReadImage(sitkUtils.GetSlicerITKReadWriteAddress(mergedLabelmapNode.GetName()))
backgroundImage = sitk.ReadImage(sitkUtils.GetSlicerITKReadWriteAddress(masterVolumeNode.GetName()))
# Run watershed filter
featureImage = sitk.GradientMagnitudeRecursiveGaussian(backgroundImage, float(self.scriptedEffect.doubleParameter("ObjectScaleMm")))
del backgroundImage
f = sitk.MorphologicalWatershedFromMarkersImageFilter()
f.SetMarkWatershedLine(False)
f.SetFullyConnected(False)
labelImage = f.Execute(featureImage, labelImage)
del featureImage
# Pixel type of watershed output is the same as the input. Convert it to int16 now.
if labelImage.GetPixelID() != sitk.sitkInt16:
labelImage = sitk.Cast(labelImage, sitk.sitkInt16)
# Write result from SimpleITK to Slicer. This currently performs a deep copy of the bulk data.
sitk.WriteImage(labelImage, sitkUtils.GetSlicerITKReadWriteAddress(mergedLabelmapNode.GetName()))
mergedLabelmapNode.GetImageData().Modified()
mergedLabelmapNode.Modified()
# Update segmentation from labelmap node and remove temporary nodes
slicer.vtkSlicerSegmentationsModuleLogic.ImportLabelmapToSegmentationNode(mergedLabelmapNode, segmentationNode, visibleSegmentIds)
slicer.mrmlScene.RemoveNode(masterVolumeNode)
slicer.mrmlScene.RemoveNode(mergedLabelmapNode)
qt.QApplication.restoreOverrideCursor()
def prepareLabelsFromSegmentation(self, segmentationNode, grayscaleImage, labelsDict):
import vtkSegmentationCorePython as vtkSegmentationCore
segLogic = slicer.modules.segmentations.logic()
segmentation = segmentationNode.GetSegmentation()
binaryRepresentationDef = vtkSegmentationCore.vtkSegmentationConverter.GetSegmentationBinaryLabelmapRepresentationName()
if not segmentation.ContainsRepresentation(binaryRepresentationDef):
segmentation.CreateRepresentation(binaryRepresentationDef)
segmentLabelmapNode = slicer.vtkMRMLLabelMapVolumeNode()
slicer.mrmlScene.AddNode(segmentLabelmapNode)
for segmentID in range(segmentation.GetNumberOfSegments()):
segment = segmentation.GetNthSegment(segmentID)
segmentLabelmap = segment.GetRepresentation(
vtkSegmentationCore.vtkSegmentationConverter.GetSegmentationBinaryLabelmapRepresentationName())
if not segLogic.CreateLabelmapVolumeFromOrientedImageData(segmentLabelmap, segmentLabelmapNode):
self.logger.error("Failed to convert label map")
return labelsDict
labelmapImage = sitk.ReadImage(sitkUtils.GetSlicerITKReadWriteAddress(segmentLabelmapNode.GetName()))
labelmapImage = self.resampleITKLabel(labelmapImage, grayscaleImage)
labelsDict[segmentationNode.GetName()+"_segment_"+segment.GetName()] = labelmapImage
displayNode = segmentLabelmapNode.GetDisplayNode()
if displayNode:
slicer.mrmlScene.RemoveNode(displayNode)
slicer.mrmlScene.RemoveNode(segmentLabelmapNode)
return labelsDict
def onFiducialNode(self, name, mrmlWidget, isPoint):
if not mrmlWidget.visible:
return
annotationFiducialNode = mrmlWidget.currentNode()
# point in physical space
coord = [0, 0, 0]
if annotationFiducialNode.GetClassName(
) == "vtkMRMLMarkupsFiducialNode":
# slicer4 Markups node
if annotationFiducialNode.GetNumberOfFiducials() < 1:
return
annotationFiducialNode.GetNthFiducialPosition(0, coord)
else:
annotationFiducialNode.GetFiducialCoordinates(coord)
# HACK transform from RAS to LPS
coord = [-coord[0], -coord[1], coord[2]]
# FIXME: we should not need to copy the image
if not isPoint and len(self.inputs) and self.inputs[0]:
imgNodeName = self.inputs[0].GetName()
img = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(imgNodeName))
coord = img.TransformPhysicalPointToIndex(coord)
exec('self.filter.Set{0}(coord)'.format(name))
def run(self, filter, outputMRMLNode, outputLabelMap, *inputs):
"""
Run the actual algorithm
"""
if self.thread.is_alive():
import sys
sys.stderr.write("FilterLogic is already executing!")
return
inputImages = []
for i in inputs:
if i is None:
break
imgNodeName = i.GetName()
img = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(imgNodeName))
inputImages.append(img)
self.output = None
# check
self.outputNodeName = outputMRMLNode.GetName()
self.outputLabelMap = outputLabelMap
self.abort = False
self.thread = threading.Thread(target=lambda f=filter, i=inputImages:
self.thread_doit(f, *inputImages))
self.main_queue_start()
self.thread.start()
def calculateFeatures(self, imageNode, labelNode, segmentationNode,
extractor):
"""
Calculate the feature on the image node for each ROI contained in the labelNode and/or the segmentation node, using
an instantiated RadiomicsFeatureExtractor (with customization already configured).
"""
# Prepare the input volume
self.logger.debug('Read the input image node')
grayscaleImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(imageNode.GetName()))
# Prepare the input label map
self.logger.debug('Read and prepare the input ROI(s)')
labelsDict = {}
if labelNode:
labelsDict = self.prepareLabelsFromLabelmap(
labelNode, grayscaleImage, labelsDict)
if segmentationNode:
labelsDict = self.prepareLabelsFromSegmentation(
segmentationNode, grayscaleImage, labelsDict)
# Calculate the features
featuresDict = {}
for l in labelsDict.keys():
self.logger.debug("Calculating features for " + l)
try:
self.logger.debug('Starting feature calculation')
featuresDict[l] = extractor.execute(grayscaleImage,
labelsDict[l])
self.logger.debug('Features calculated')
except:
self.logger.error('calculateFeatures() failed')
traceback.print_exc()
return featuresDict
def run(self, imageNode, labelNode, segmentationNode, featureClasses,
**kwargs):
"""
Run the actual algorithm
"""
self.logger.info('Processing started')
grayscaleImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(imageNode.GetName()))
#sitkUtils.PushToSlicer(label, labelNode.GetName(), overwrite=True, compositeView=2)
labelsDict = {}
if labelNode:
labelsDict = self.prepareLabelsFromLabelmap(
labelNode, grayscaleImage, labelsDict)
if segmentationNode:
labelsDict = self.prepareLabelsFromSegmentation(
segmentationNode, grayscaleImage, labelsDict)
#self.featureValues = extractor.execute(grayscaleImage, labelImage, image, **kwargs)
featuresDict = {}
for l in labelsDict.keys():
self.logger.debug("Calculating features for " + l)
featuresDict[l] = self.calculateFeatures(grayscaleImage,
labelsDict[l],
featureClasses, **kwargs)
return featuresDict
def _getLabelGeneratorFromLabelMap(self, labelNode, imageNode):
combinedLabelImage = sitk.ReadImage(sitkUtils.GetSlicerITKReadWriteAddress(labelNode.GetName()))
combinedLabelArray = sitk.GetArrayFromImage(combinedLabelImage)
labels = numpy.unique(combinedLabelArray)
for l in labels:
if l == 0:
continue
yield '%s_label_%d' % (labelNode.GetName(), l), labelNode, int(l), imageNode
def onInputSelect(self, mrmlNode, n):
self.inputs[n] = mrmlNode
if n == 0 and self.inputs[0]:
# if the input zero is a label assume the output is too, test widgets
self.onOutputLabelMapChanged(mrmlNode.GetLabelMap())
imgNodeName = self.inputs[0].GetName()
img = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(imgNodeName))
def doit(self):
import SimpleITK as sitk
import sitkUtils
labelLogic = self.sliceLogic.GetLabelLayer()
labelNode = labelLogic.GetVolumeNode()
labelNodeName = labelNode.GetName()
labelImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(labelNodeName))
backgroundLogic = self.sliceLogic.GetBackgroundLayer()
backgroundNode = backgroundLogic.GetVolumeNode()
backgroundNodeName = backgroundNode.GetName()
backgroundImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(backgroundNodeName))
# store a backup copy of the label map for undo
# (this happens in it's own thread, so it is cheap)
if self.undoRedo:
self.undoRedo.saveState()
featureImage = sitk.GradientMagnitudeRecursiveGaussian(
backgroundImage, float(self.sigma))
del backgroundImage
f = sitk.MorphologicalWatershedFromMarkersImageFilter()
f.SetMarkWatershedLine(False)
f.SetFullyConnected(False)
labelImage = f.Execute(featureImage, labelImage)
del featureImage
# The output of the watershed is the same as the input.
# But Slicer expects labelMaps to be Int16, so convert to that
# type to improve compatibility, just in case if needed.
if labelImage.GetPixelID() != sitk.sitkInt16:
labelImage = sitk.Cast(labelImage, sitk.sitkInt16)
# This currently performs a deep copy of the bulk data.
sitk.WriteImage(labelImage,
sitkUtils.GetSlicerITKReadWriteAddress(labelNodeName))
labelNode.GetImageData().Modified()
labelNode.Modified()
def getDice(reference, moving):
moving = runBRAINSResample(moving, reference)
referenceAddress = sitkUtils.GetSlicerITKReadWriteAddress(
reference.GetName())
image_reference = sitk.ReadImage(referenceAddress)
movingAddress = sitkUtils.GetSlicerITKReadWriteAddress(moving.GetName())
image_input = sitk.ReadImage(movingAddress)
# make sure both labels have the same value
threshold = sitk.BinaryThresholdImageFilter()
threshold.SetUpperThreshold(100)
threshold.SetLowerThreshold(1)
threshold.SetInsideValue(1)
image_reference = threshold.Execute(image_reference)
image_input = threshold.Execute(image_input)
measureFilter = sitk.LabelOverlapMeasuresImageFilter()
if measureFilter.Execute(image_reference, image_input):
print 'filter executed'
value = measureFilter.GetDiceCoefficient()
return value
def prepareLabelsFromLabelmap(self, labelmapNode, grayscaleImage, labelsDict):
combinedLabelImage = sitk.ReadImage(sitkUtils.GetSlicerITKReadWriteAddress(labelmapNode.GetName()))
resampledLabelImage = self.resampleITKLabel(combinedLabelImage, grayscaleImage)
ls = sitk.LabelStatisticsImageFilter()
ls.Execute(resampledLabelImage,resampledLabelImage)
th = sitk.BinaryThresholdImageFilter()
th.SetInsideValue(1)
th.SetOutsideValue(0)
for l in ls.GetLabels()[1:]:
th.SetUpperThreshold(l)
th.SetLowerThreshold(l)
labelsDict[labelmapNode.GetName()+"_label_"+str(l)] = th.Execute(combinedLabelImage)
return labelsDict
def onFiducialListNode(self, name, mrmlNode):
annotationHierarchyNode = mrmlNode
# list of points in physical space
coords = []
if annotationHierarchyNode.GetClassName(
) == "vtkMRMLMarkupsFiducialNode":
# slicer4 Markups node
for i in range(annotationHierarchyNode.GetNumberOfFiducials()):
coord = [0, 0, 0]
annotationHierarchyNode.GetNthFiducialPosition(i, coord)
coords.append(coord)
else:
# slicer4 style hierarchy nodes
# get the first in the list
for listIndex in range(
annotationHierarchyNode.GetNumberOfChildrenNodes()):
if annotationHierarchyNode.GetNthChildNode(listIndex) is None:
continue
annotation = annotationHierarchyNode.GetNthChildNode(
listIndex).GetAssociatedNode()
if annotation is None:
continue
coord = [0, 0, 0]
annotation.GetFiducialCoordinates(coord)
coords.append(coord)
if self.inputs[0]:
imgNodeName = self.inputs[0].GetName()
img = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(imgNodeName))
# HACK transform from RAS to LPS
coords = [[-pt[0], -pt[1], pt[2]] for pt in coords]
idx_coords = [
img.TransformPhysicalPointToIndex(pt) for pt in coords
]
exec('self.filter.Set{0}(idx_coords)'.format(name))
def updateOutput(self, img):
nodeWriteAddress = sitkUtils.GetSlicerITKReadWriteAddress(
self.outputNodeName)
sitk.WriteImage(img, nodeWriteAddress)
node = slicer.util.getNode(self.outputNodeName)
applicationLogic = slicer.app.applicationLogic()
selectionNode = applicationLogic.GetSelectionNode()
if self.outputLabelMap:
selectionNode.SetReferenceActiveLabelVolumeID(node.GetID())
else:
selectionNode.SetReferenceActiveVolumeID(node.GetID())
applicationLogic.PropagateVolumeSelection(0)
applicationLogic.FitSliceToAll()
def getCentroidForLabel(labelNode, value):
if not labelNode:
return None
labelAddress = sitkUtils.GetSlicerITKReadWriteAddress(
labelNode.GetName())
labelImage = sitk.ReadImage(labelAddress)
ls = sitk.LabelStatisticsImageFilter()
ls.Execute(labelImage, labelImage)
bb = ls.GetBoundingBox(value)
centroid = None # sagittal, coronal, axial
if len(bb) > 0:
centerIJK = [((bb[0] + bb[1]) / 2), ((bb[2] + bb[3]) / 2),
((bb[4] + bb[5]) / 2)]
logging.debug('BB is: ' + str(bb))
logging.debug('i_center = ' + str(centerIJK[0]))
logging.debug('j_center = ' + str(centerIJK[1]))
logging.debug('k_center = ' + str(centerIJK[2]))
IJKtoRAS = vtk.vtkMatrix4x4()
labelNode.GetIJKToRASMatrix(IJKtoRAS)
IJKtoRASDir = vtk.vtkMatrix4x4()
labelNode.GetIJKToRASDirectionMatrix(IJKtoRASDir)
RAScoord = IJKtoRAS.MultiplyPoint(
(centerIJK[0], centerIJK[1], centerIJK[2], 1))
order = labelNode.ComputeScanOrderFromIJKToRAS(IJKtoRAS)
if order == 'IS':
RASDir = IJKtoRASDir.MultiplyPoint(
(RAScoord[0], RAScoord[1], RAScoord[2], 1))
centroid = [-RASDir[0], -RASDir[1], RASDir[2]]
elif order == 'AP':
RASDir = IJKtoRASDir.MultiplyPoint(
(RAScoord[0], RAScoord[1], RAScoord[2], 1))
centroid = [-RASDir[0], -RASDir[2], -RASDir[1]]
elif order == 'LR':
RASDir = IJKtoRASDir.MultiplyPoint(
(RAScoord[2], RAScoord[1], RAScoord[0], 1))
centroid = [RASDir[0], -RASDir[2], -RASDir[1]]
return centroid
def __init__(self, grayscaleNode, labelNode, fileName=None):
#import numpy
self.keys = [
"Label", "Count", "Volume mm^3", "Volume cc", "Min", "Max", "Mean",
"StdDev"
]
cubicMMPerVoxel = reduce(lambda x, y: x * y, labelNode.GetSpacing())
ccPerCubicMM = 0.001
# TODO: progress and status updates
# this->InvokeEvent(vtkLabelStatisticsLogic::StartLabelStats, (void*)"start label stats")
self.labelStats = {}
self.labelStats['Labels'] = []
labelNodeName = labelNode.GetName()
labelImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(labelNodeName))
grayscaleNodeName = grayscaleNode.GetName()
grayscaleImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(grayscaleNodeName))
sitkStats = sitk.LabelStatisticsImageFilter()
sitkStats.Execute(grayscaleImage, labelImage)
for l in sitkStats.GetLabels():
# add an entry to the LabelStats list
self.labelStats["Labels"].append(l)
self.labelStats[l, "Label"] = l
self.labelStats[l, "Count"] = sitkStats.GetCount(l)
self.labelStats[
l,
"Volume mm^3"] = self.labelStats[l, "Count"] * cubicMMPerVoxel
self.labelStats[
l,
"Volume cc"] = self.labelStats[l, "Volume mm^3"] * ccPerCubicMM
self.labelStats[l, "Min"] = sitkStats.GetMinimum(l)
self.labelStats[l, "Max"] = sitkStats.GetMaximum(l)
self.labelStats[l, "Mean"] = sitkStats.GetMean(l)
self.labelStats[l, "StdDev"] = sitkStats.GetSigma(l)
self.labelStats[l, "Sum"] = sitkStats.GetSum(l)
del sitkStats
sitkShapeStats = sitk.LabelShapeStatisticsImageFilter()
sitkShapeStats.ComputeFeretDiameterOff()
sitkShapeStats.ComputePerimeterOn()
sitkShapeStats.Execute(labelImage)
# use a set to accumulate attributes to make sure they are unuque
shapeAttributes = [
# 'Number Of Pixels',
# 'Physical Size',
# 'Centroid',
# 'Bounding Box',
'Number Of Pixels On Border',
'Perimeter On Border',
'Perimeter On Border Ratio',
# 'Principal Moments',
'Principal Axes',
'Elongation',
'Perimeter',
'Roundness',
'Equivalent Spherical Radius',
'Equivalent Spherical Perimeter',
# 'Equivalent Ellipsoid Diameter',
'Flatness',
'Feret Diameter'
]
if not sitkShapeStats.GetComputeFeretDiameter():
shapeAttributes.remove('Feret Diameter')
if not sitkShapeStats.GetComputePerimeter():
shapeAttributes.remove('Perimeter')
# We don't have a good way to show
shapeAttributes.remove('Principal Axes')
self.keys += shapeAttributes
for l in sitkShapeStats.GetLabels():
# add attributes form the Shape label object
for name in shapeAttributes:
attr = getattr(sitkShapeStats,
"Get" + name.replace(' ', ''))(l)
self.labelStats[l, name] = attr
for l in sitkShapeStats.GetLabels():
attr = getattr(sitkShapeStats, "Get" + "PrincipalMoments")(l)
for i in range(1, 4):
self.labelStats[l, "Principal Moments " + str(i)] = attr[i - 1]
self.keys += ["Principal Moments " + str(i) for i in range(1, 4)]
def createModels(self):
self.deleteModels()
self.labelScores = []
self.selectedLableList = []
if self.calcinationType == 0 and self.volumeNode and self.roiNode:
#print 'in Heart Create Models'
slicer.vtkSlicerCropVolumeLogic().CropVoxelBased(self.roiNode, self.volumeNode, self.croppedNode)
croppedImage = sitk.ReadImage( sitkUtils.GetSlicerITKReadWriteAddress(self.croppedNode.GetName()))
thresholdImage = sitk.BinaryThreshold(croppedImage,self.ThresholdMin, self.ThresholdMax, 1, 0)
connectedCompImage =sitk.ConnectedComponent(thresholdImage, True)
relabelImage =sitk.RelabelComponent(connectedCompImage)
labelStatFilter =sitk.LabelStatisticsImageFilter()
labelStatFilter.Execute(croppedImage, relabelImage)
if relabelImage.GetPixelID() != sitk.sitkInt16:
relabelImage = sitk.Cast( relabelImage, sitk.sitkInt16 )
sitk.WriteImage( relabelImage, sitkUtils.GetSlicerITKReadWriteAddress(self.labelsNode.GetName()))
nLabels = labelStatFilter.GetNumberOfLabels()
#print "Number of labels = ", nLabels
self.totalScore = 0
count = 0
for n in range(0,nLabels):
max = labelStatFilter.GetMaximum(n);
size = labelStatFilter.GetCount(n)
score = self.computeScore(max)
if size*self.voxelVolume > self.MaximumLesionSize:
continue
if size*self.voxelVolume < self.MinimumLesionSize:
nLabels = n+1
break
#print "label = ", n, " max = ", max, " score = ", score, " voxels = ", size
self.labelScores.append(score)
self.selectedLableList.append(0)
self.marchingCubes.SetInputData(self.labelsNode.GetImageData())
self.marchingCubes.SetValue(0, n)
self.marchingCubes.Update()
self.transformPolyData.SetInputData(self.marchingCubes.GetOutput())
mat = vtk.vtkMatrix4x4()
self.labelsNode.GetIJKToRASMatrix(mat)
trans = vtk.vtkTransform()
trans.SetMatrix(mat)
self.transformPolyData.SetTransform(trans)
self.transformPolyData.Update()
poly = vtk.vtkPolyData()
poly.DeepCopy(self.transformPolyData.GetOutput())
modelNode = slicer.vtkMRMLModelNode()
slicer.mrmlScene.AddNode(modelNode)
dnode = slicer.vtkMRMLModelDisplayNode()
slicer.mrmlScene.AddNode(dnode)
modelNode.AddAndObserveDisplayNodeID(dnode.GetID())
modelNode.SetAndObservePolyData(poly)
ct=slicer.mrmlScene.GetNodeByID('vtkMRMLColorTableNodeLabels')
rgb = [0,0,0]
ct.GetLookupTable().GetColor(count+1,rgb)
dnode.SetColor(rgb)
self.addLabel(count, rgb, score)
self.modelNodes.append(modelNode)
self.selectedLables[poly] = n
count = count+1
#a = slicer.util.array(tn.GetID())
#sa = sitk.GetImageFromArray(a)
self.scoreField.setText(self.totalScore)
else:
print "not implemented"
def preProcessLabel(self, labelNodeID, bbMin, bbMax):
print('Label node ID: ' + labelNodeID)
labelNode = slicer.util.getNode(labelNodeID)
labelNodeAddress = sitkUtils.GetSlicerITKReadWriteAddress(
labelNode.GetName())
print('Label node address: ' + str(labelNodeAddress))
labelImage = sitk.ReadImage(labelNodeAddress)
print('Read image: ' + str(labelImage))
crop = sitk.CropImageFilter()
crop.SetLowerBoundaryCropSize(bbMin)
crop.SetUpperBoundaryCropSize(bbMax)
croppedImage = crop.Execute(labelImage)
print('Cropped image done: ' + str(croppedImage))
croppedLabelName = labelNode.GetName() + '-Cropped'
sitkUtils.PushToSlicer(croppedImage, croppedLabelName, overwrite=True)
print('Cropped volume pushed')
croppedLabel = slicer.util.getNode(croppedLabelName)
print('Smoothed image done')
smoothLabelName = labelNode.GetName() + '-Smoothed'
smoothLabel = self.createVolumeNode(smoothLabelName)
# smooth the labels
smoothingParameters = {
'inputImageName': croppedLabel.GetID(),
'outputImageName': smoothLabel.GetID()
}
print(str(smoothingParameters))
cliNode = slicer.cli.run(slicer.modules.segmentationsmoothing,
None,
smoothingParameters,
wait_for_completion=True)
# crop the bounding box
'''
TODO:
* output volume node probably not needed here
* intermediate nodes should probably be hidden
'''
dt = sitk.SignedMaurerDistanceMapImageFilter()
dt.SetSquaredDistance(False)
distanceMapName = labelNode.GetName() + '-DistanceMap'
print('Reading smoothed image: ' + smoothLabel.GetID())
smoothLabelAddress = sitkUtils.GetSlicerITKReadWriteAddress(
smoothLabel.GetName())
smoothLabelImage = sitk.ReadImage(smoothLabelAddress)
print(smoothLabelAddress)
distanceImage = dt.Execute(smoothLabelImage)
sitkUtils.PushToSlicer(distanceImage, distanceMapName, overwrite=True)
return slicer.util.getNode(distanceMapName)
def getStartEndWithConnectedComponents(self, volume, center):
address = sitkUtils.GetSlicerITKReadWriteAddress(volume.GetName())
image = sitk.ReadImage(address)
start = self.getStartSliceUsingConnectedComponents(center, image)
end = self.getEndSliceUsingConnectedComponents(center, image)
return start, end
def createModels(self):
# Reset previous model and labels
self.deleteModels()
for sr in self.summary_reports:
self.labelScores[sr] = []
self.selectedLabelList = []
if self.calcificationType == 0 and self.volumeNode and self.roiNode:
slicer.vtkSlicerCropVolumeLogic().CropVoxelBased(
self.roiNode, self.volumeNode, self.croppedNode)
croppedImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(
self.croppedNode.GetName()))
thresholdImage = sitk.BinaryThreshold(croppedImage,
self.ThresholdMin,
self.ThresholdMax, 1, 0)
connectedCompImage = sitk.ConnectedComponent(thresholdImage, True)
relabelImage = sitk.RelabelComponent(connectedCompImage)
labelStatFilter = sitk.LabelStatisticsImageFilter()
labelStatFilter.Execute(croppedImage, relabelImage)
if relabelImage.GetPixelID() != sitk.sitkInt16:
relabelImage = sitk.Cast(relabelImage, sitk.sitkInt16)
sitk.WriteImage(
relabelImage,
sitkUtils.GetSlicerITKReadWriteAddress(
self.labelsNode.GetName()))
prod_spacing = np.prod(croppedImage.GetSpacing())
nLabels = labelStatFilter.GetNumberOfLabels()
self.totalScore = 0
count = 0
for n in range(0, nLabels):
max = labelStatFilter.GetMaximum(n)
mean = labelStatFilter.GetMean(n)
size = labelStatFilter.GetCount(n)
volume = size * self.voxelVolume
# current label is discarted if volume not meet the maximum allowed threshold
if volume > self.MaximumLesionSize:
continue
# As ordered, we stop here if the volume of the current label is less than threshold
if volume < self.MinimumLesionSize:
nLabels = n + 1
break
score2d, volume, mass_score = self.agatston_computation(
n, relabelImage, croppedImage, prod_spacing)
# Agatston 3d:
# -----------
density_score = self.computeDensityScore(max)
score3d = size * (self.sx * self.sy) * density_score
mass_score = mean * volume
# self.labelScores["Agatston Score"].append(score)
self.labelScores["Agatston Score 3D"].append(score3d)
self.labelScores["Agatston Score 2D"].append(score2d)
self.labelScores["Mass Score"].append(mass_score)
self.labelScores["Volume"].append(volume)
self.selectedLabelList.append(0)
# generate the contour
marchingCubes = vtk.vtkDiscreteMarchingCubes()
marchingCubes.SetInputData(self.labelsNode.GetImageData())
marchingCubes.SetValue(0, count + 1)
marchingCubes.Update()
transformPolyData = vtk.vtkTransformPolyDataFilter()
transformPolyData.SetInputData(marchingCubes.GetOutput())
mat = vtk.vtkMatrix4x4()
self.labelsNode.GetIJKToRASMatrix(mat)
trans = vtk.vtkTransform()
trans.SetMatrix(mat)
transformPolyData.SetTransform(trans)
transformPolyData.Update()
poly = vtk.vtkPolyData()
poly.DeepCopy(transformPolyData.GetOutput())
modelNode = slicer.vtkMRMLModelNode()
slicer.mrmlScene.AddNode(modelNode)
dnode = slicer.vtkMRMLModelDisplayNode()
slicer.mrmlScene.AddNode(dnode)
modelNode.AddAndObserveDisplayNodeID(dnode.GetID())
modelNode.SetAndObservePolyData(poly)
ct = slicer.mrmlScene.GetNodeByID(
'vtkMRMLColorTableNodeLabels')
rgb = [0, 0, 0]
ct.GetLookupTable().GetColor(count + 1, rgb)
dnode.SetColor(rgb)
# Enable Slice intersection
dnode.SetSliceDisplayMode(0)
dnode.SetSliceIntersectionVisibility(1)
# self.addLabel(count, rgb, [score, mass_score, volume, mean, max])
self.addLabel(
count, rgb,
[score2d, score3d, mass_score, volume, mean, max])
count = count + 1
self.modelNodes.append(modelNode)
self.selectedLabels[poly] = n
for sr in self.summary_reports:
self.scoreField[sr].setText(self.totalScores[sr])
def createModels(self):
self.deleteModels()
for sr in self.summary_reports:
self.labelScores[sr]=[]
self.selectedLabelList = []
if self.calcificationType == 0 and self.volumeNode and self.roiNode:
#print 'in Heart Create Models'
slicer.vtkSlicerCropVolumeLogic().CropVoxelBased(self.roiNode, self.volumeNode, self.croppedNode)
croppedImage = sitk.ReadImage( sitkUtils.GetSlicerITKReadWriteAddress(self.croppedNode.GetName()))
thresholdImage = sitk.BinaryThreshold(croppedImage,self.ThresholdMin, self.ThresholdMax, 1, 0)
connectedCompImage =sitk.ConnectedComponent(thresholdImage, True)
relabelImage =sitk.RelabelComponent(connectedCompImage)
labelStatFilter =sitk.LabelStatisticsImageFilter()
labelStatFilter.Execute(croppedImage, relabelImage)
if relabelImage.GetPixelID() != sitk.sitkInt16:
relabelImage = sitk.Cast( relabelImage, sitk.sitkInt16 )
sitk.WriteImage( relabelImage, sitkUtils.GetSlicerITKReadWriteAddress(self.labelsNode.GetName()))
nLabels = labelStatFilter.GetNumberOfLabels()
#print "Number of labels = ", nLabels
self.totalScore = 0
count = 0
#Computation of the score follows this paper:
#C. H McCollough, Radiology, 243(2), 2007
for n in range(0,nLabels):
max = labelStatFilter.GetMaximum(n)
mean = labelStatFilter.GetMean(n)
size = labelStatFilter.GetCount(n)
volume = size*self.voxelVolume
if volume > self.MaximumLesionSize:
continue
if volume < self.MinimumLesionSize:
nLabels = n+1
break
density_score = self.computeDensityScore(max)
#Agatston score is \sum_i area_i * density_score_i
#For now we assume that all the plaques have the same density score
score = size*(self.sx*self.sy)*density_score
mass_score = mean*volume
#print "label = ", n, " max = ", max, " score = ", score, " voxels = ", size
self.labelScores["Agatston Score"].append(score)
self.labelScores["Mass Score"].append(mass_score)
self.labelScores["Volume"].append(volume)
self.selectedLabelList.append(0)
self.marchingCubes.SetInputData(self.labelsNode.GetImageData())
self.marchingCubes.SetValue(0, n)
self.marchingCubes.Update()
self.transformPolyData.SetInputData(self.marchingCubes.GetOutput())
mat = vtk.vtkMatrix4x4()
self.labelsNode.GetIJKToRASMatrix(mat)
trans = vtk.vtkTransform()
trans.SetMatrix(mat)
self.transformPolyData.SetTransform(trans)
self.transformPolyData.Update()
poly = vtk.vtkPolyData()
poly.DeepCopy(self.transformPolyData.GetOutput())
modelNode = slicer.vtkMRMLModelNode()
slicer.mrmlScene.AddNode(modelNode)
dnode = slicer.vtkMRMLModelDisplayNode()
slicer.mrmlScene.AddNode(dnode)
modelNode.AddAndObserveDisplayNodeID(dnode.GetID())
modelNode.SetAndObservePolyData(poly)
ct=slicer.mrmlScene.GetNodeByID('vtkMRMLColorTableNodeLabels')
rgb = [0,0,0]
ct.GetLookupTable().GetColor(count+1,rgb)
dnode.SetColor(rgb)
#Enable Slice intersection
dnode.SetSliceDisplayMode(0)
dnode.SetSliceIntersectionVisibility(1)
self.addLabel(count, rgb, [score,mass_score,volume,mean,max])
count = count+1
self.modelNodes.append(modelNode)
self.selectedLabels[poly] = n
#a = slicer.util.array(tn.GetID())
#sa = sitk.GetImageFromArray(a)
for sr in self.summary_reports:
self.scoreField[sr].setText(self.totalScores[sr])
else:
print ("not implemented")
def doit(self):
labelLogic = self.sliceLogic.GetLabelLayer()
labelNode = labelLogic.GetVolumeNode()
labelNodeName = labelNode.GetName()
labelImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(labelNodeName))
bgLogic = self.sliceLogic.GetBackgroundLayer()
bgNode = bgLogic.GetVolumeNode()
bgNodeName = bgNode.GetName()
bgImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(bgNodeName))
# store a backup copy of the label map for undo
# (this happens in it's own thread, so it is cheap)
if self.undoRedo:
self.undoRedo.saveState()
labelID = self.editUtil.getLabel()
l = sitk.BinaryThreshold(labelImage, labelID, labelID, 1, 0)
filled = sitk.BinaryFillhole(l)
d = sitk.SignedMaurerDistanceMap(filled,
insideIsPositive=False,
squaredDistance=False,
useImageSpacing=True)
del filled
d = sitk.Threshold(d, -1e23, 0, 0)
feature = d
if (self.splitSize != 0.0):
# the splitSize is divided by 2 to convert from a diameter size of a radius size
level = self.splitSize * 0.5
d = sitk.HMinima(feature, height=level, fullyConnected=False)
markers = sitk.RegionalMinima(d,
backgroundValue=0,
foregroundValue=1,
fullyConnected=False,
flatIsMinima=True)
del d
markers = sitk.ConnectedComponent(markers, fullyConnected=False)
ws = sitk.MorphologicalWatershedFromMarkers(feature,
markers,
markWatershedLine=False)
del feature
del markers
ws = sitk.Mask(sitk.Cast(ws, labelImage.GetPixelIDValue()), l)
del l
sitk.WriteImage(ws,
sitkUtils.GetSlicerITKReadWriteAddress(labelNodeName))
labelNode.GetImageData().Modified()
labelNode.Modified()
def refineLandmark(self, state):
"""Refine the specified landmark"""
# Refine landmark, or if none, do nothing
# Crop images around the fiducial
# Affine registration of the cropped images
# Transform the fiducial using the transformation
#
# No need to take into account the current transformation because landmarks are in World RAS
timing = False
if self.VerboseMode == "Verbose":
timing = True
if state.fixed == None or state.moving == None or state.fixedFiducials == None or state.movingFiducials == None or state.currentLandmarkName == None:
print "Cannot refine landmarks. Images or landmarks not selected."
return
print("Refining landmark " +
state.currentLandmarkName) + " using " + self.name
start = time.time()
if timing: loadStart = start
volumes = (state.fixed, state.moving)
(fixedVolume, movingVolume) = volumes
fixedImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(fixedVolume.GetName()))
movingImage = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(movingVolume.GetName()))
if timing:
print 'Time for loading was ' + str(time.time() -
loadStart) + ' seconds'
landmarks = state.logic.landmarksForVolumes(volumes)
(fixedFiducial, movingFiducial) = landmarks[state.currentLandmarkName]
(fixedList, fixedIndex) = fixedFiducial
(movingList, movingIndex) = movingFiducial
fixedPoint = [
0,
] * 3
movingPoint = [
0,
] * 3
fixedList.GetNthFiducialPosition(fixedIndex, fixedPoint)
movingList.GetNthFiducialPosition(movingIndex, movingPoint)
# HACK transform from RAS to LPS
fixedPoint = [-fixedPoint[0], -fixedPoint[1], fixedPoint[2]]
movingPoint = [-movingPoint[0], -movingPoint[1], movingPoint[2]]
# NOTE: SimpleITK index always starts at 0
# define an roi for the fixed
if timing: roiStart = time.time()
fixedRadius = 30
fixedROISize = [
0,
] * 3
fixedROIIndex = [
0,
] * 3
fixedROIIndex = list(
fixedImage.TransformPhysicalPointToIndex(fixedPoint))
for i in range(3):
if fixedROIIndex[i] < 0 or fixedROIIndex[i] > fixedImage.GetSize(
)[i] - 1:
import sys
sys.stderr.write(
"Fixed landmark {0} in not with in fixed image!\n".format(
landmarkName))
return
radius = min(fixedRadius, fixedROIIndex[i],
fixedImage.GetSize()[i] - fixedROIIndex[i] - 1)
fixedROISize[i] = radius * 2 + 1
fixedROIIndex[i] -= radius
if self.VerboseMode == "Full Verbose":
print "Fixed ROI: ", fixedROIIndex, fixedROISize
if timing: roiEnd = time.time()
# crop the fixed
if timing: cropStart = time.time()
croppedFixedImage = sitk.RegionOfInterest(fixedImage, fixedROISize,
fixedROIIndex)
croppedFixedImage = sitk.Cast(croppedFixedImage, sitk.sitkFloat32)
if timing: cropEnd = time.time()
# define an roi for the moving
if timing: roi2Start = time.time()
if self.LocalSimpleITKMode == "Small":
movingRadius = 45
else:
movingRadius = 60
movingROISize = [
0,
] * 3
movingROIIndex = [
0,
] * 3
movingROIIndex = list(
movingImage.TransformPhysicalPointToIndex(movingPoint))
for i in range(3):
if movingROIIndex[i] < 0 or movingROIIndex[
i] > movingImage.GetSize()[i] - 1:
import sys
sys.stderr.write(
"Moving landmark {0} in not with in moving image!\n".
format(landmarkName))
return
radius = min(movingRadius, movingROIIndex[i],
movingImage.GetSize()[i] - movingROIIndex[i] - 1)
movingROISize[i] = radius * 2 + 1
movingROIIndex[i] -= radius
if self.VerboseMode == "Full Verbose":
print "Moving ROI: ", movingROIIndex, movingROISize
if timing: roi2End = time.time()
if timing: crop2Start = time.time()
croppedMovingImage = sitk.RegionOfInterest(movingImage, movingROISize,
movingROIIndex)
croppedMovingImage = sitk.Cast(croppedMovingImage, sitk.sitkFloat32)
if timing: crop2End = time.time()
if timing:
print 'Time to set up fixed ROI was ' + str(roiEnd -
roiStart) + ' seconds'
if timing:
print 'Time to set up moving ROI was ' + str(
roi2End - roi2Start) + ' seconds'
if timing:
print 'Time to crop fixed volume ' + str(cropEnd -
cropStart) + ' seconds'
if timing:
print 'Time to crop moving volume ' + str(crop2End -
crop2Start) + ' seconds'
# initialize the registration
if timing: initTransformStart = time.time()
tx = sitk.CenteredTransformInitializer(
croppedFixedImage, croppedMovingImage,
sitk.VersorRigid3DTransform(),
sitk.CenteredTransformInitializerFilter.GEOMETRY)
if timing: initTransformEnd = time.time()
if timing:
print 'Time to initialize transformation was ' + str(
initTransformEnd - initTransformStart) + ' seconds'
# define the registration
R = sitk.ImageRegistrationMethod()
R.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)
R.SetMetricSamplingPercentage(0.2)
R.SetMetricSamplingStrategy(sitk.ImageRegistrationMethod.RANDOM)
R.SetOptimizerAsRegularStepGradientDescent(learningRate=1,
minStep=0.1,
relaxationFactor=0.5,
numberOfIterations=250)
R.SetOptimizerScalesFromJacobian(
) # Use this for versor based transforms
R.SetShrinkFactorsPerLevel([1])
R.SetSmoothingSigmasPerLevel([1])
R.SetInitialTransform(tx)
R.SetInterpolator(sitk.sitkLinear)
#R.SetNumberOfThreads(1)
# setup an observer
def command_iteration(method):
print("{0:3} = {1:10.5f} : {2}".format(
method.GetOptimizerIteration(), method.GetMetricValue(),
method.GetOptimizerPosition()))
if self.VerboseMode == "Full Verbose":
R.AddCommand(sitk.sitkIterationEvent, lambda: command_iteration(R))
# run the registration
if timing: regStart = time.time()
outTx = R.Execute(croppedFixedImage, croppedMovingImage)
if self.VerboseMode == "Full Verbose":
print("-------")
print(outTx)
print("Optimizer stop condition: {0}".format(
R.GetOptimizerStopConditionDescription()))
print(" Iteration: {0}".format(R.GetOptimizerIteration()))
print(" Metric value: {0}".format(R.GetMetricValue()))
if timing: regEnd = time.time()
if timing:
print 'Time for local registration was ' + str(
regEnd - regStart) + ' seconds'
# apply the local transform to the landmark
#print transform
if timing: resultStart = time.time()
#outTx.SetInverse()
updatedPoint = outTx.TransformPoint(fixedPoint)
# HACK transform from LPS to RAS
updatedPoint = [-updatedPoint[0], -updatedPoint[1], updatedPoint[2]]
movingList.SetNthFiducialPosition(movingIndex, updatedPoint[0],
updatedPoint[1], updatedPoint[2])
if timing: resultEnd = time.time()
if timing:
print 'Time for transforming landmark was ' + str(
resultEnd - resultStart) + ' seconds'
end = time.time()
print 'Refined landmark ' + state.currentLandmarkName + ' in ' + str(
end - start) + ' seconds'
def run(self, dockerPath, dockerVolumePath, inputVolume):
"""
Run the actual algorithm
"""
qt.QApplication.setOverrideCursor(qt.Qt.WaitCursor)
if not self.isValidInputOutputData(inputVolume):
slicer.util.errorDisplay(
'Input volume is the same as output vessel model. Choose a different output vessel model.'
)
return False
slicer.app.processEvents()
fileList = os.listdir(dockerVolumePath)
for fileName in fileList:
os.remove(os.path.join(dockerVolumePath, fileName))
oriVolumeDim = inputVolume.GetImageData().GetDimensions()
inputVolumeSpacing = inputVolume.GetSpacing()
spacing = (inputVolumeSpacing[0] * oriVolumeDim[0] / self.DOCKERVOLUMEDIMENSION[0], \
inputVolumeSpacing[1] * oriVolumeDim[1] / self.DOCKERVOLUMEDIMENSION[1], \
inputVolumeSpacing[2] * oriVolumeDim[2] / self.DOCKERVOLUMEDIMENSION[2])
self.resampledVolume = self.ResampleVolume(inputVolume, spacing,
self.resampledVolume)
img = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(
self.resampledVolume.GetName()))
fileName = "quarterVolume.nii.gz"
sitk.WriteImage(img, str(os.path.join(dockerVolumePath, fileName)))
#-----------------------------------
logging.info('Processing started')
cmd = list()
cmd.append(dockerPath)
cmd.extend(('run', '-t', '-v'))
cmd.append(str(dockerVolumePath) + ':' + "/workspace/data/Case1")
cmd.append(
'li3igtlab/brain-vessel-seg@sha256:2095141606837f364a857ec90a19efe49f8879918881a24a4cf0e72c74a2c2d2'
)
cmd.extend(('python3', '/workspace/NiftyNet/net_run.py', 'inference'))
cmd.extend(('-a', 'brainVesselSegApp.brainVesselSegApp', '-c',
'/workspace/data/vesselSeg.ini'))
p = subprocess.Popen(cmd, stdout=subprocess.PIPE)
print cmd
progress = 0
while True:
progress += 0.15
slicer.app.processEvents()
line = p.stdout.readline()
if not line:
print "no line"
break
print(line)
slicer.app.processEvents()
successful, probabilityMap = slicer.util.loadVolume(os.path.join(
dockerVolumePath, "Case1__niftynet_out.nii.gz"),
returnNode=True)
if successful and probabilityMap:
inputVolumeSpacing = probabilityMap.GetSpacing()
spacing = (inputVolumeSpacing[0] * self.DOCKERVOLUMEDIMENSION[0] / oriVolumeDim[0] , \
inputVolumeSpacing[1] * self.DOCKERVOLUMEDIMENSION[1] / oriVolumeDim[1] , \
inputVolumeSpacing[2] * self.DOCKERVOLUMEDIMENSION[2] / oriVolumeDim[2] )
probabilityMap_resampled = self.ResampleVolume(
probabilityMap, spacing, resampledVolume=None)
probabilityMap_resampled.SetName("ProbabilityMap_Resampled")
slicer.mrmlScene.AddNode(probabilityMap_resampled)
inputVolume.SetAttribute(self.REL_PROBABLITYMAP,
probabilityMap_resampled.GetID())
logging.info('Processing completed')
qt.QApplication.restoreOverrideCursor()
return True
def run(self, leftVol, rightVol, enableScreenshots=0):
"""
Run the actual algorithm
"""
logging.info('Processing started')
nodeName = "BlankVolume"
imageSize = [390, 466, 318]
voxelType = vtk.VTK_UNSIGNED_CHAR
imageOrigin = [98, 98, -72]
imageSpacing = [0.5, 0.5, 0.5]
imageDirections = [[-1, 0, 0], [0, -1, 0], [0, 0, 1]]
fillVoxelValue = 255
# Create an empty image volume, filled with fillVoxelValue
imageData = vtk.vtkImageData()
imageData.SetDimensions(imageSize)
imageData.AllocateScalars(voxelType, 1)
thresholder = vtk.vtkImageThreshold()
thresholder.SetInputData(imageData)
thresholder.SetInValue(fillVoxelValue)
thresholder.SetOutValue(fillVoxelValue)
thresholder.Update()
# Create volume node
blankVolumeNode = slicer.mrmlScene.AddNewNodeByClass(
"vtkMRMLScalarVolumeNode", nodeName)
blankVolumeNode.SetOrigin(imageOrigin)
blankVolumeNode.SetSpacing(imageSpacing)
blankVolumeNode.SetIJKToRASDirections(imageDirections)
blankVolumeNode.SetAndObserveImageData(thresholder.GetOutput())
blankVolumeNode.CreateDefaultDisplayNodes()
blankVolumeNode.CreateDefaultStorageNode()
logging.info('Created empty volume Blank Volume')
# left right front back bottom top
leftData = leftVol.GetPolyData().GetPoints().GetBounds()
rightData = rightVol.GetPolyData().GetPoints().GetBounds()
leftBound = leftData[0]
rightBound = rightData[1]
topBound = max(leftData[5], rightData[5])
frontBound = min(leftData[2], rightData[2])
backBound = max(leftData[3], rightData[3])
midline = (leftBound + rightBound) / 2
halfway = -(backBound + frontBound) / 1.7
print(leftBound, rightBound)
print("Midline:", midline)
print(frontBound, backBound)
print("Halfway:", halfway)
print("Top:", topBound)
# handleNode.SetAndObservePolyData(handle.GetOutput())
# handleNode.CreateDefaultDisplayNodes()
def makeHandle():
fn = vtk.vtkParametricTorus()
fn.SetRingRadius((rightBound - leftBound) / 5)
fn.SetCrossSectionRadius((rightBound - leftBound) / 15)
#vtk.FlipNormalsOn()
source = vtk.vtkParametricFunctionSource()
source.SetParametricFunction(fn)
source.Update()
trans = vtk.vtkTransform()
trans.RotateX(90)
trans.Translate(midline, topBound + 5, halfway)
# vtk generate normals
# communicate with SLACK
rotate = vtk.vtkTransformPolyDataFilter()
rotate.SetTransform(trans)
rotate.SetInputConnection(source.GetOutputPort())
rotate.Update()
return rotate.GetOutput()
handleVol = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLModelNode',
"handle")
handle = makeHandle()
handleVol.SetAndObservePolyData(handle)
handleVol.CreateDefaultDisplayNodes()
handleVol.CreateDefaultStorageNode()
leftBLM = slicer.mrmlScene.AddNewNodeByClass(
'vtkMRMLLabelMapVolumeNode', "leftBLM")
# leftBLM.CreateDefaultDisplayNodes()
# leftBLM.CreateDefaultStorageNode()
rightBLM = slicer.mrmlScene.AddNewNodeByClass(
'vtkMRMLLabelMapVolumeNode', "rightBLM")
# rightBLM.CreateDefaultDisplayNodes()
# rightBLM.CreateDefaultStorageNode()
handleBLM = slicer.mrmlScene.AddNewNodeByClass(
'vtkMRMLLabelMapVolumeNode', "handleBLM")
# handleBLM.CreateDefaultDisplayNodes()
# handleBLM.CreateDefaultStorageNode()
leftParams = {
'sampleDistance': 0.1,
'InputVolume': blankVolumeNode.GetID(),
'surface': leftVol.GetID(),
'OutputVolume': leftBLM.GetID()
}
process = slicer.cli.run(slicer.modules.modeltolabelmap,
None,
leftParams,
wait_for_completion=True)
rightParams = {
'sampleDistance': 0.1,
'InputVolume': blankVolumeNode.GetID(),
'surface': rightVol.GetID(),
'OutputVolume': rightBLM.GetID()
}
process = slicer.cli.run(slicer.modules.modeltolabelmap,
None,
rightParams,
wait_for_completion=True)
# we have leftBLM, rightBLM, and handleBLM; all are binary label maps
brainBLM = slicer.mrmlScene.AddNewNodeByClass(
'vtkMRMLLabelMapVolumeNode', "brainBLM")
# brainBLM.CreateDefaultDisplayNodes()
# brainBLM.CreateDefaultStorageNode()
path = slicer.util.tempDirectory("saves")
leftPath = path + "/left.nrrd"
rightPath = path + "/right.nrrd"
slicer.util.saveNode(leftBLM, leftPath)
slicer.util.saveNode(rightBLM, rightPath)
left = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(leftBLM.GetName()))
right = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(rightBLM.GetName()))
orFilter = sitk.OrImageFilter()
brain = orFilter.Execute(right, left)
dilateFilter = sitk.BinaryDilateImageFilter()
rad = round((rightBound - leftBound) / 30)
dilateFilter.SetKernelRadius(rad)
dilateFilter.SetBackgroundValue(0)
dilateFilter.SetForegroundValue(255)
print(dilateFilter.GetKernelType(), dilateFilter.GetKernelRadius())
brain_dilated = dilateFilter.Execute(brain)
holesFilter = sitk.BinaryFillholeImageFilter()
brain_dilated_fixed = holesFilter.Execute(brain_dilated, True, 255)
#output = orFilter.Execute(intermediate, handle)
sitkUtils.PushToSlicer(brain_dilated_fixed,
"combined sides",
compositeView=0,
overwrite=False)
slicer.mrmlScene.RemoveNode(leftBLM)
slicer.mrmlScene.RemoveNode(rightBLM)
slicer.mrmlScene.RemoveNode(handleBLM)
slicer.mrmlScene.RemoveNode(brainBLM)
slicer.mrmlScene.RemoveNode(blankVolumeNode)
# Capture screenshot
if enableScreenshots:
self.takeScreenshot('MySlicerExtensionTest-Start', 'MyScreenshot',
-1)
logging.info('Processing completed')
return True
def run(self, leftVol, rightVol, enableScreenshots=0):
"""
Run the actual algorithm
"""
logging.info('Processing started')
nodeName = "BlankVolume"
imageSize = [390, 466, 318]
voxelType = vtk.VTK_UNSIGNED_CHAR
imageOrigin = [98, 98, -72]
imageSpacing = [0.5, 0.5, 0.5]
imageDirections = [[-1, 0, 0], [0, -1, 0], [0, 0, 1]]
fillVoxelValue = 255
# Create an empty image volume, filled with fillVoxelValue
imageData = vtk.vtkImageData()
imageData.SetDimensions(imageSize)
imageData.AllocateScalars(voxelType, 1)
thresholder = vtk.vtkImageThreshold()
thresholder.SetInputData(imageData)
thresholder.SetInValue(fillVoxelValue)
thresholder.SetOutValue(fillVoxelValue)
thresholder.Update()
# Create volume node
blankVolumeNode = slicer.mrmlScene.AddNewNodeByClass(
"vtkMRMLScalarVolumeNode", nodeName)
blankVolumeNode.SetOrigin(imageOrigin)
blankVolumeNode.SetSpacing(imageSpacing)
blankVolumeNode.SetIJKToRASDirections(imageDirections)
blankVolumeNode.SetAndObserveImageData(thresholder.GetOutput())
blankVolumeNode.CreateDefaultDisplayNodes()
blankVolumeNode.CreateDefaultStorageNode()
logging.info('Created empty volume Blank Volume')
# left right front back bottom top
leftData = leftVol.GetPolyData().GetPoints().GetBounds()
rightData = rightVol.GetPolyData().GetPoints().GetBounds()
leftBound = leftData[0]
rightBound = rightData[1]
topBound = max(leftData[5], rightData[5])
frontBound = min(leftData[2], rightData[2])
backBound = max(leftData[3], rightData[3])
midline = (leftBound + rightBound) / 2
halfway = -(backBound + frontBound) / 1.7
print(leftBound, rightBound)
# medial could be constant zero but why not account for lateral brain asymmetry
print("Medial:", midline)
print("Coronal:", halfway)
print("Top Bound:", topBound)
# function for creating solid taurus and positioning using coronal/medial/top bound measurements
def makeHandle():
fn = vtk.vtkParametricTorus()
# scale radius by a factor of the brain width
fn.SetRingRadius((rightBound - leftBound) / 5)
fn.SetCrossSectionRadius((rightBound - leftBound) / 15)
source = vtk.vtkParametricFunctionSource()
source.SetParametricFunction(fn)
source.Update()
trans = vtk.vtkTransform()
trans.RotateX(90)
# add a bit extra to top bound so it sticks out
trans.Translate(midline, topBound + 5, halfway)
rotate = vtk.vtkTransformPolyDataFilter()
rotate.SetTransform(trans)
rotate.SetInputConnection(source.GetOutputPort())
rotate.Update()
return rotate.GetOutput()
handleVol = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLModelNode',
"handle")
handle = makeHandle()
handleVol.SetAndObservePolyData(handle)
handleVol.CreateDefaultDisplayNodes()
handleVol.CreateDefaultStorageNode()
# add nodes to scene
leftBLM = slicer.mrmlScene.AddNewNodeByClass(
'vtkMRMLLabelMapVolumeNode', "leftBLM")
rightBLM = slicer.mrmlScene.AddNewNodeByClass(
'vtkMRMLLabelMapVolumeNode', "rightBLM")
handleBLM = slicer.mrmlScene.AddNewNodeByClass(
'vtkMRMLLabelMapVolumeNode', "handleBLM")
# manually create parameters for the left and right maps for ModelToLabelMap module
leftParams = {
'sampleDistance': 0.1,
'InputVolume': blankVolumeNode.GetID(),
'surface': leftVol.GetID(),
'OutputVolume': leftBLM.GetID()
}
process = slicer.cli.run(slicer.modules.modeltolabelmap,
None,
leftParams,
wait_for_completion=True)
rightParams = {
'sampleDistance': 0.1,
'InputVolume': blankVolumeNode.GetID(),
'surface': rightVol.GetID(),
'OutputVolume': rightBLM.GetID()
}
process = slicer.cli.run(slicer.modules.modeltolabelmap,
None,
rightParams,
wait_for_completion=True)
# we have leftBLM, rightBLM, and handleBLM; all are binary label maps
brainBLM = slicer.mrmlScene.AddNewNodeByClass(
'vtkMRMLLabelMapVolumeNode', "brainBLM")
path = slicer.util.tempDirectory("saves")
leftPath = path + "/left.nrrd"
rightPath = path + "/right.nrrd"
slicer.util.saveNode(leftBLM, leftPath)
slicer.util.saveNode(rightBLM, rightPath)
# move to simple itk in order to Or function the binary label maps
# look into doing this manually by looping thru voxels and bitwise oring them maybe?
left = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(leftBLM.GetName()))
right = sitk.ReadImage(
sitkUtils.GetSlicerITKReadWriteAddress(rightBLM.GetName()))
orFilter = sitk.OrImageFilter()
brain = orFilter.Execute(right, left)
# dilate the image to increase structural integrity of the 3D printed Model and make it more aesthetic
dilateFilter = sitk.BinaryDilateImageFilter()
rad = round((rightBound - leftBound) / 30)
dilateFilter.SetKernelRadius(rad)
dilateFilter.SetBackgroundValue(0)
dilateFilter.SetForegroundValue(255)
print(dilateFilter.GetKernelType(), dilateFilter.GetKernelRadius())
brain_dilated = dilateFilter.Execute(brain)
holesFilter = sitk.BinaryFillholeImageFilter()
brain_dilated_fixed = holesFilter.Execute(brain_dilated, True, 255)
# push SimpleITK back to Slicer and clean up the extra nodes
sitkUtils.PushToSlicer(brain_dilated_fixed,
"combined sides",
compositeView=0,
overwrite=False)
slicer.mrmlScene.RemoveNode(leftBLM)
slicer.mrmlScene.RemoveNode(rightBLM)
slicer.mrmlScene.RemoveNode(handleBLM)
slicer.mrmlScene.RemoveNode(brainBLM)
slicer.mrmlScene.RemoveNode(blankVolumeNode)
# TODO: convert to a different type so that it's ready to go with 3D printing, makes it easier when running on a batch of .nrrd files
# Capture screenshot
if enableScreenshots:
self.takeScreenshot('MySlicerExtensionTest-Start', 'MyScreenshot',
-1)
logging.info('Processing completed')
return True
