forked from jackyko1991/Intracranial-CFD
/
auto_vessel_seg.py
211 lines (167 loc) · 7.12 KB
/
auto_vessel_seg.py
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import os
import SimpleITK as sitk
import vtk
def ExtractLargestConnectedComponents(label):
ccFilter = sitk.ConnectedComponentImageFilter()
label = ccFilter.Execute(label)
labelStat = sitk.LabelShapeStatisticsImageFilter()
labelStat.Execute(label)
largestVol = 0
largestLabel = 0
for labelNum in labelStat.GetLabels():
if labelStat.GetPhysicalSize(labelNum) > largestVol:
largestVol = labelStat.GetPhysicalSize(labelNum)
largestLabel = labelNum
thresholdFilter = sitk.BinaryThresholdImageFilter()
thresholdFilter.SetLowerThreshold(largestLabel)
thresholdFilter.SetUpperThreshold(largestLabel)
thresholdFilter.SetInsideValue(1)
thresholdFilter.SetOutsideValue(0)
label = thresholdFilter.Execute(label)
return label
def Extract3DRA(img_path,vessel_path):
print("Extracting vessel from 3DRA data:",img_path)
reader = sitk.ImageFileReader()
reader.SetFileName(img_path)
img = reader.Execute()
otsuFilter = sitk.OtsuMultipleThresholdsImageFilter()
otsuFilter.SetNumberOfHistogramBins (256)
otsuFilter.SetNumberOfThresholds(3)
seg = otsuFilter.Execute(img)
thresholdFilter = sitk.BinaryThresholdImageFilter()
thresholdFilter.SetInsideValue (1)
thresholdFilter.SetLowerThreshold(3)
thresholdFilter.SetOutsideValue (0)
thresholdFilter.SetUpperThreshold(4)
vessel_seg = thresholdFilter.Execute(seg)
vessel_seg = ExtractLargestConnectedComponents(vessel_seg)
writer = sitk.ImageFileWriter()
writer.SetFileName(vessel_path)
writer.Execute(vessel_seg)
# thresholdFilter = sitk.BinaryThresholdImageFilter()
# thresholdFilter.SetInsideValue (1)
# thresholdFilter.SetLowerThreshold(2)
# thresholdFilter.SetOutsideValue (0)
# thresholdFilter.SetUpperThreshold(2.1)
# bone_seg = thresholdFilter.Execute(seg)
# openingFilter = sitk.BinaryMorphologicalOpeningImageFilter()
# openingFilter.SetKernelType(1)
# openingFilter.SetKernelRadius(2)
# bone_seg = openingFilter.Execute(bone_seg)
# writer = sitk.ImageFileWriter()
# writer.SetFileName(bone_path)
# writer.Execute(bone_seg)
def ExtractCBCT(img_path,vessel_path):
print("Extracting vessel from CBCT data:",img_path)
reader = sitk.ImageFileReader()
reader.SetFileName(img_path)
img = reader.Execute()
# reader.SetFileName(bone_mask_path)
# bone_mask = reader.Execute()
otsuFilter = sitk.OtsuMultipleThresholdsImageFilter()
otsuFilter.SetNumberOfHistogramBins (256)
otsuFilter.SetNumberOfThresholds(4)
seg = otsuFilter.Execute(img)
# thresholdFilter = sitk.ThresholdImageFilter()
# # thresholdFilter.SetInsideValue (1)
# thresholdFilter.SetLower(2) # 2 and/or 3, depends on data
# thresholdFilter.SetUpper(3.1)
# thresholdFilter.SetOutsideValue (0)
# vessel_seg = thresholdFilter.Execute(seg)
thresholdFilter = sitk.BinaryThresholdImageFilter()
thresholdFilter.SetInsideValue (1)
thresholdFilter.SetOutsideValue (0)
thresholdFilter.SetLowerThreshold(3) # 2 and/or 3, depends on data
thresholdFilter.SetUpperThreshold(3.1)
vessel_seg = thresholdFilter.Execute(seg)
fillingFilter = sitk.BinaryFillholeImageFilter()
vessel_seg = fillingFilter.Execute(vessel_seg)
vessel_seg = ExtractLargestConnectedComponents(vessel_seg)
writer = sitk.ImageFileWriter()
writer.SetFileName(vessel_path)
writer.Execute(vessel_seg)
def LabelToSurface(label_path, surface_path, lcc=False):
print("Converting segmentation label to surface file:",label_path)
# binary path
bin_path = "D:/Dr_Simon_Yu/CFD_intracranial/code/cxx/label_to_mesh/build/Release/LabelToMesh.exe"
# convert to binary file
command = bin_path + " " + label_path + " " + surface_path + " 1 1"
# print(command)
os.system(command)
reader = vtk.vtkGenericDataObjectReader()
reader.SetFileName(surface_path)
reader.Update()
surface = reader.GetOutput()
transform = vtk.vtkTransform()
transform.Scale(-1,-1,1)
transformFilter = vtk.vtkTransformPolyDataFilter()
transformFilter.SetInputData(surface)
transformFilter.SetTransform(transform)
transformFilter.Update()
surface = transformFilter.GetOutput()
smoothFilter = vtk.vtkSmoothPolyDataFilter()
smoothFilter.SetInputData(surface)
smoothFilter.SetNumberOfIterations(15);
smoothFilter.SetRelaxationFactor(0.1);
smoothFilter.FeatureEdgeSmoothingOff();
smoothFilter.BoundarySmoothingOn();
smoothFilter.Update();
surface = smoothFilter.GetOutput()
if lcc:
connectedFilter = vtk.vtkConnectivityFilter()
connectedFilter.SetInputData(surface)
connectedFilter.Update()
surface = connectedFilter.GetOutput()
writer = vtk.vtkGenericDataObjectWriter()
writer.SetFileName(surface_path)
writer.SetInputData(surface)
writer.Update()
def crop_defected_region(image_path, defected_point_csv_path, cropped_image_path,crop_size=[50,50,50]):
reader = sitk.ImageFileReader()
reader.SetFileName(image_path)
image = reader.Execute()
defected_point = open(defected_point_csv_path,"r").readlines()[3]
defected_point = defected_point.split(",")[1:4]
defected_point = [float(i) for i in defected_point]
defected_point[0] = -1*defected_point[0]
defected_point[1] = -1*defected_point[1]
defected_index = image.TransformPhysicalPointToIndex(defected_point)
start_point = [defected_point[i]-crop_size[i]/2 for i in range(3)]
end_point = [defected_point[i]+crop_size[i]/2 for i in range(3)]
start_indices = list(image.TransformPhysicalPointToIndex(start_point))
end_indices = list(image.TransformPhysicalPointToIndex(end_point))
new_size = []
for i in range(3):
if start_indices[i] < 0:
start_indices[i] = 0
if start_indices[i] >= image.GetSize()[i]:
start_indices[i] = image.GetSize()[i]-1
if end_indices[i] < 0:
end_indices[i] = 0
if end_indices[i] >= image.GetSize()[i]:
end_indices[i] = image.GetSize()[i]-1
size_ = abs(end_indices[i]-start_indices[i])
if start_indices[i] > end_indices[i]:
tmp = start_indices[i]
start_indices[i] = end_indices[i]
end_indices[i] = tmp
new_size.append(size_)
roiFilter = sitk.RegionOfInterestImageFilter()
roiFilter.SetSize(new_size)
roiFilter.SetIndex(start_indices)
image = roiFilter.Execute(image)
writer = sitk.ImageFileWriter()
writer.SetFileName(cropped_image_path)
writer.Execute(image)
def main():
data_folder = "D:/Dr_Simon_Yu/CFD_intracranial/data/comparison/BlasiRaquelLegaspi"
# Extract3DRA(data_folder + "baseline/3DRA.nii",data_folder + "baseline/seg_vessel.nii")
# Extract3DRA(data_folder + "baseline-post/3DRA.nii",data_folder + "baseline-post/seg_vessel.nii")
# Extract3DRA(data_folder + "12months/3DRA.nii",data_folder + "12months/seg_vessel.nii")
# ExtractCBCT(data_folder + "followup/CBCT.nii",data_folder + "followup/seg_vessel.nii")
# LabelToSurface(os.path.join(data_folder,"3DRA_seg.nii.gz"), os.path.join(data_folder,"surface.vtk"))
crop_defected_region(os.path.join(data_folder,"3DRA","3DRA.nii"), os.path.join(data_folder,"defected_point.fcsv"),os.path.join(data_folder,"3DRA","3DRA_cropped.nii.gz"))
# crop_defected_region(os.path.join(data_folder,"3DRA","3DRA_seg.nii.gz"), os.path.join(data_folder,"defected_point.fcsv"),os.path.join(data_folder,"3DRA","3DRA_seg_cropped.nii.gz"))
crop_defected_region(os.path.join(data_folder,"CBCT","CBCT_reg.nii"), os.path.join(data_folder,"defected_point.fcsv"),os.path.join(data_folder,"CBCT","CBCT_reg_cropped.nii.gz"))
if __name__ == "__main__":
main()