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Prostate.py
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573 lines (481 loc) · 25 KB
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import os
import sys
sys.argv = '1'
import unittest
import logging
import vtk, qt, ctk, slicer
from slicer.ScriptedLoadableModule import *
from slicer.util import VTKObservationMixin
import tensorflow.keras.models as M
import tensorflow.keras.backend as K
import cv2
import numpy as np
from skimage.io import imsave, imread
from skimage.transform import resize
from skimage.util import img_as_float
import open3d as o3d
from skimage.measure import label
import SegmentRegistration
### NEED TO INSTALL IN SLICER BEFORE IMPORTING THE MODULE ###
# pip_install('opencv-python==4.5.4.58')
# pip_install('tensorflow')
# pip_install('keras==2.6.0')
#pip_install('scikit-image')
#pip_install('open3d==0.14.1')
class Prostate(ScriptedLoadableModule):
"""Uses ScriptedLoadableModule base class, available at:
https://github.com/Slicer/Slicer/blob/master/Base/Python/slicer/ScriptedLoadableModule.py
"""
def __init__(self, parent):
ScriptedLoadableModule.__init__(self, parent)
self.parent.title = "Prostate" # TODO: make this more human readable by adding spaces
self.parent.categories = ["Examples"] # TODO: set categories (folders where the module shows up in the module selector)
self.parent.dependencies = [] # TODO: add here list of module names that this module requires
self.parent.contributors = ["John Doe (AnyWare Corp.)"] # TODO: replace with "Firstname Lastname (Organization)"
# TODO: update with short description of the module and a link to online module documentation
self.parent.helpText = """
This is an example of scripted loadable module bundled in an extension.
See more information in <a href="https://github.com/organization/projectname#Prostate">module documentation</a>.
"""
# TODO: replace with organization, grant and thanks
self.parent.acknowledgementText = """
This file was originally developed by Jean-Christophe Fillion-Robin, Kitware Inc., Andras Lasso, PerkLab,
and Steve Pieper, Isomics, Inc. and was partially funded by NIH grant 3P41RR013218-12S1.
"""
# Additional initialization step after application startup is complete
slicer.app.connect("startupCompleted()", registerSampleData)
#
# Register sample data sets in Sample Data module
#
def registerSampleData():
"""
Add data sets to Sample Data module.
"""
# It is always recommended to provide sample data for users to make it easy to try the module,
# but if no sample data is available then this method (and associated startupCompeted signal connection) can be removed.
import SampleData
iconsPath = os.path.join(os.path.dirname(__file__), 'Resources/Icons')
# To ensure that the source code repository remains small (can be downloaded and installed quickly)
# it is recommended to store data sets that are larger than a few MB in a Github release.
# Prostate1
SampleData.SampleDataLogic.registerCustomSampleDataSource(
# Category and sample name displayed in Sample Data module
category='Prostate',
sampleName='Prostate1',
# Thumbnail should have size of approximately 260x280 pixels and stored in Resources/Icons folder.
# It can be created by Screen Capture module, "Capture all views" option enabled, "Number of images" set to "Single".
thumbnailFileName=os.path.join(iconsPath, 'Prostate1.png'),
# Download URL and target file name
uris="https://github.com/Slicer/SlicerTestingData/releases/download/SHA256/998cb522173839c78657f4bc0ea907cea09fd04e44601f17c82ea27927937b95",
fileNames='Prostate1.nrrd',
# Checksum to ensure file integrity. Can be computed by this command:
# import hashlib; print(hashlib.sha256(open(filename, "rb").read()).hexdigest())
checksums = 'SHA256:998cb522173839c78657f4bc0ea907cea09fd04e44601f17c82ea27927937b95',
# This node name will be used when the data set is loaded
nodeNames='Prostate1'
)
# Prostate2
SampleData.SampleDataLogic.registerCustomSampleDataSource(
# Category and sample name displayed in Sample Data module
category='Prostate',
sampleName='Prostate2',
thumbnailFileName=os.path.join(iconsPath, 'Prostate2.png'),
# Download URL and target file name
uris="https://github.com/Slicer/SlicerTestingData/releases/download/SHA256/1a64f3f422eb3d1c9b093d1a18da354b13bcf307907c66317e2463ee530b7a97",
fileNames='Prostate2.nrrd',
checksums = 'SHA256:1a64f3f422eb3d1c9b093d1a18da354b13bcf307907c66317e2463ee530b7a97',
# This node name will be used when the data set is loaded
nodeNames='Prostate2'
)
#
# ProstateWidget
#
class ProstateWidget(ScriptedLoadableModuleWidget, VTKObservationMixin):
"""Uses ScriptedLoadableModuleWidget base class, available at:
https://github.com/Slicer/Slicer/blob/master/Base/Python/slicer/ScriptedLoadableModule.py
"""
def __init__(self, parent=None):
"""
Called when the user opens the module the first time and the widget is initialized.
"""
ScriptedLoadableModuleWidget.__init__(self, parent)
VTKObservationMixin.__init__(self) # needed for parameter node observation
self.logic = None
slicer.mymod = self
self.path = os.path.dirname(os.path.abspath(__file__))
self._parameterNode = None
self._updatingGUIFromParameterNode = False
self.smooth = 1
self.UNetModel = M.load_model(self.path + './Resources/Models/'+'Prostate.h5', custom_objects={'dice_coef_loss': self.dice_coef_loss,'dice_coef':self.dice_coef})
self.UNetModel.load_weights(self.path + './Resources/Models/'+'Prostate.h5', by_name=True)
K.set_image_data_format('channels_last') # TF dimension ordering in this code
self.edge = vtk.vtkFeatureEdges()
self.edge.BoundaryEdgesOn()
self.edge.FeatureEdgesOn()
self.edge.ManifoldEdgesOff()
self.img_rows = 256
self.img_cols = 256
self.ijktoRasTransform = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLLinearTransformNode')
self.shiftTransform = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLLinearTransformNode')
self.ijktoRasTransform.SetName('IJKToRAS Transform')
self.ijkToRasMat = vtk.vtkMatrix4x4()
self.shift = vtk.vtkMatrix4x4()
self.resliceLogic = slicer.modules.volumereslicedriver.logic()
self.Immean = None
# self.reslicedImage = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLScalarVolumeNode')
self.prostateSeg = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLSegmentationNode')
self.count = 1
self.segLog = slicer.modules.segmentations.logic()
self.inputVolume =None
self.APD = vtk.vtkAppendPolyData()
self.firstSeg = True
self.inputVolume = None
self.setSlice = True
def setup(self):
"""
Called when the user opens the module the first time and the widget is initialized.
"""
ScriptedLoadableModuleWidget.setup(self)
# Load widget from .ui file (created by Qt Designer).
# Additional widgets can be instantiated manually and added to self.layout.
uiWidget = slicer.util.loadUI(self.resourcePath('UI/Prostate.ui'))
self.layout.addWidget(uiWidget)
self.ui = slicer.util.childWidgetVariables(uiWidget)
# Set scene in MRML widgets. Make sure that in Qt designer the top-level qMRMLWidget's
# "mrmlSceneChanged(vtkMRMLScene*)" signal in is connected to each MRML widget's.
# "setMRMLScene(vtkMRMLScene*)" slot.
uiWidget.setMRMLScene(slicer.mrmlScene)
# These connections ensure that whenever user changes some settings on the GUI, that is saved in the MRML scene
# (in the selected parameter node).
self.ui.inputSelector.connect("currentNodeChanged(vtkMRMLNode*)", self.onInputVolumeChanged)
# Reconstruction button
self.ui.setSliceButton.connect('clicked(bool)', self.onsetSliceClicked)
# Registration buttons
self.ui.modelSelector.connect("currentNodeChanged(vtkMRMLNode*)", self.onModelChanged)
self.ui.MRVolumeSelector.connect("currentNodeChanged(vtkMRMLNode*)", self.onMRVolumeChanged)
self.ui.registerButton.connect('clicked(bool)', self.onRegClicked)
self.ui.zoneSelector.connect("currentNodeChanged(vtkMRMLNode*)", self.onZoneChanged)
self.ui.invertButton.connect('clicked(bool)', self.onInvertClicked)
# Metrics dropdowns/buttons
self.ui.GTSelector.connect("currentNodeChanged(vtkMRMLNode*)", self.onGTChanged)
self.ui.RegZoneSelector.connect("currentNodeChanged(vtkMRMLNode*)", self.onPredChanged)
self.ui.metricsButton.connect('clicked(bool)', self.metrics)
# Make sure parameter node is initialized (needed for module reload)
'''Method to set the ground truth model'''
def onGTChanged(self):
self.GTNode = self.ui.GTSelector.currentNode()
'''Method to set the predicted/registered model'''
def onPredChanged(self):
self.predNode = self.ui.RegZoneSelector.currentNode()
'''Method to set the input volume -this will always be the Ultrasound volume'''
def onInputVolumeChanged(self):
# if self.ui.inputSelector.currentNode() is None:
self.inputVolume = self.ui.inputSelector.currentNode()
slicer.app.layoutManager().setLayout(slicer.vtkMRMLLayoutNode.SlicerLayoutFourUpView)
self.ui.setSliceButton.enabled=True
'''Method to set the MR volume'''
def onMRVolumeChanged(self):
self.MRVolume = self.ui.MRVolumeSelector.currentNode()
# it auto rotates 90 degrees to match with the reconstructed prostate. Might not be necessary with different types of data
T = vtk.vtkTransform()
T.RotateX(-90)
rotateTransform = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLLinearTransformNode')
rotateTransform.SetAndObserveTransformToParent(T)
self.MRVolume.SetAndObserveTransformNodeID(rotateTransform.GetID())
self.MRVolume.HardenTransform()
slicer.mrmlScene.RemoveNode(rotateTransform)
'''Method to set the MRI prostate capsule model'''
def onModelChanged(self):
self.modelNode = self.ui.modelSelector.currentNode()
self.ui.registerButton.enabled=True
# it auto rotates 90 degrees to match with the reconstructed prostate. Might not be necessary with different types of data
T = vtk.vtkTransform()
T.RotateX(-90)
rotateTransform = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLLinearTransformNode')
rotateTransform.SetAndObserveTransformToParent(T)
self.modelNode.SetAndObserveTransformNodeID(rotateTransform.GetID())
self.modelNode.HardenTransform()
slicer.mrmlScene.RemoveNode(rotateTransform)
'''Method to set the PZ model. This model should originate from the same data as the MRI volume and MRI prostate capsule'''
def onZoneChanged(self):
self.pzNode = self.ui.zoneSelector.currentNode()
self.ui.invertButton.enabled=True
# it auto rotates 90 degrees to match with the reconstructed prostate. Might not be necessary with different types of data
T = vtk.vtkTransform()
T.RotateX(-90)
rotateTransform = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLLinearTransformNode')
rotateTransform.SetAndObserveTransformToParent(T)
self.pzNode.SetAndObserveTransformNodeID(rotateTransform.GetID())
self.pzNode.HardenTransform()
slicer.mrmlScene.RemoveNode(rotateTransform)
'''Method to set up the deformable registration. This makes use of the Segment Registration module'''
def onRegClicked(self):
s = slicer.mrmlScene
# make the segmentations from the models
self.modelSeg = s.AddNewNodeByClass("vtkMRMLSegmentationNode", "MR") #MRI segmentation
self.usSeg = s.AddNewNodeByClass("vtkMRMLSegmentationNode", "US") #US segmentation
self.segLog.ImportModelToSegmentationNode(self.modelNode, self.modelSeg)
self.segLog.ImportModelToSegmentationNode(self.finalModel, self.usSeg)
widget = SegmentRegistration.SegmentRegistrationWidget() # this will open the segment registration widget -user input required
'''Method to fix all the transforms to set the PZ on the TRUS properly'''
def onInvertClicked(self):
s = slicer.mrmlScene
prealign = s.GetFirstNodeByName("PreAlignmentMoving2FixedLinearTransform")
self.inputVolume.SetAndObserveTransformNodeID(None) #remove the transform from the TRUS
#prealign.Inverse()
self.finalModel.SetAndObserveTransformNodeID(prealign.GetID()) #shift the TRUS model to new coordinates
# hide the segmentations
displayNode = self.usSeg.GetDisplayNode()
displayNode.SetAllSegmentsVisibility(False)
displayNode = self.modelSeg.GetDisplayNode()
displayNode.SetAllSegmentsVisibility(False)
# get and invert the transform. Apply it to the MRI model
transform = s.GetFirstNodeByName( "Deformable Transform")
transform.Inverse()
self.modelNode.SetAndObserveTransformNodeID(transform.GetID())
# apply to the zone
self.pzNode.SetAndObserveTransformNodeID(transform.GetID())
'''Some methods necessary for running the U-net'''
def dice_coef_loss(self, y_true, y_pred):
return -dice_coef(y_true, y_pred)
def dice_coef(self, y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + self.smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + self.smooth)
def predict(self,im):
im = im/255.
self.x = np.expand_dims( cv2.resize(im,[256,256]), axis=2)
self.out = self.UNetModel.predict(np.expand_dims(self.x, axis=0))
'''Method to run the reconstruction step'''
def onsetSliceClicked(self):
inputVolumeNode = self.inputVolume
observerTag = None
spacing = inputVolumeNode.GetSpacing()
outputSpacing = [spacing[0],spacing[1],spacing[2]] # Millimeters/pixel
A = slicer.util.arrayFromVolume(inputVolumeNode)
outputExtent = [0, int(A.shape[1])-1 , 0, int(A.shape[0])-1, 0,int(spacing[2]*100*2)-1]# First and last pixel indices along each axis
# Compute reslice transformation
self.volumeToIjkMatrix = vtk.vtkMatrix4x4()
inputVolumeNode.GetRASToIJKMatrix(self.volumeToIjkMatrix)
b = [0,0,0,0,0,0]
inputVolumeNode.GetBounds(b)
T = vtk.vtkTransform()
T.RotateX(-90)
centerTransform = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLLinearTransformNode')
centerTransform.SetAndObserveTransformToParent(T)
inputVolumeNode.SetAndObserveTransformNodeID(centerTransform.GetID())
self.sliceToRasTransform = vtk.vtkTransform()
self.sliceToRasTransform.Translate(b[0], -b[-1], 0)
self.sliceToIjkTransform = vtk.vtkTransform()
self.sliceToIjkTransform.Concatenate(self.volumeToIjkMatrix)
self.sliceToIjkTransform.Concatenate(self.sliceToRasTransform)
# Use MRML node to modify transform in GUI
sliceToRasNode = slicer.mrmlScene.GetFirstNodeByName("SliceToRas")
if sliceToRasNode is None:
sliceToRasNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLLinearTransformNode", "SliceToRas")
sliceToRasNode.SetAndObserveTransformToParent(self.sliceToRasTransform)
# Run reslice to produce output image
self.reslice = vtk.vtkImageReslice()
self.reslice.SetInputData(inputVolumeNode.GetImageData())
self.reslice.SetResliceTransform(self.sliceToIjkTransform)
self.reslice.SetInterpolationModeToLinear()
self.reslice.SetOutputOrigin(0.0, 0.0, 0.0) # Must keep zero so transform overlays slice with volume
self.reslice.SetOutputSpacing(outputSpacing)
self.reslice.SetOutputDimensionality(2)
self.reslice.SetOutputExtent(outputExtent)
self.reslice.SetBackgroundLevel(0)
self.reslice.Update()
self.reslice.GetOutput().SetSpacing(1,1,1) # Spacing will be set on MRML node to let Slicer know
# To allow re-run of this script, try to reuse exisiting node before creating a new one
outputNode = slicer.mrmlScene.GetFirstNodeByName("OutputVolume")
if outputNode is None:
outputNode = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLScalarVolumeNode", "OutputVolume")
outputImageData = self.reslice.GetOutput()
outputNode.SetAndObserveImageData(self.reslice.GetOutput())
outputNode.SetSpacing(outputSpacing)
outputNode.CreateDefaultDisplayNodes()
# Transform output image so it is aligned with original volume
outputNode.SetAndObserveTransformNodeID(sliceToRasNode.GetID())
# Show and follow output image in red slice view
redSliceWidget = slicer.app.layoutManager().sliceWidget("Red")
redSliceWidget.sliceController().setSliceVisible(True)
redSliceWidget.sliceLogic().GetSliceCompositeNode().SetBackgroundVolumeID(outputNode.GetID())
driver = slicer.modules.volumereslicedriver.logic()
redView = slicer.util.getNode('vtkMRMLSliceNodeRed')
driver.SetModeForSlice(driver.MODE_TRANSVERSE, redView)
driver.SetDriverForSlice(outputNode.GetID(), redView)
if observerTag is not None:
sliceToRasNode.RemoveObserver(observerTag)
observerTag = None
observerTag = sliceToRasNode.AddObserver(sliceToRasNode.TransformModifiedEvent, self.updateReslice)
self.applyRotation(outputNode, 0,sliceToRasNode)
self.sliceToRasNode = sliceToRasNode
self.performRecon()
inputVolumeNode.HardenTransform()
'''Method which allows the radial slicing and reconstruction to run in one click'''
def updateReslice(self, event, caller):
try:
slicer.app.pauseRender()
inputTransformId = self.inputVolume.GetTransformNodeID()
if inputTransformId is not None:
inputTransformNode = slicer.mrmlScene.GetNodeByID(inputTransformId)
rasToVolumeMatrix = vtk.vtkMatrix4x4()
inputTransformNode.GetMatrixTransformFromWorld(rasToVolumeMatrix)
self.sliceToIjkTransform.Identity()
self.sliceToIjkTransform.Concatenate(self.volumeToIjkMatrix)
self.sliceToIjkTransform.Concatenate(rasToVolumeMatrix)
self.sliceToIjkTransform.Concatenate(self.sliceToRasTransform)
self.reslice.Update()
self.reslice.GetOutput().SetSpacing(1,1,1)
finally:
slicer.app.resumeRender()
'''Method to rotate the TRUS to the correct orientation for the U-Net'''
def applyRotation(self,outputNode,deg,sliceToRasNode):
b = [0,0,0,0,0,0]
self.inputVolume.GetBounds(b)
rotate = vtk.vtkTransform()
rotate.RotateY(deg)
rotate.Translate(b[0], -b[-1], 0)
mat = vtk.vtkMatrix4x4()
rotate.GetMatrix(mat)
sliceToRasNode.SetMatrixTransformToParent(mat)
outputNode.GetDisplayNode().SetWindowLevelMinMax(0,178)
'''Method to segment each radial slice'''
def getSegmentationFromSlice(self, outputNode, sliceToRasNode,deg):
self.npImage = np.squeeze(slicer.util.arrayFromVolume(outputNode))
b = [0,0,0,0,0,0]
outputNode.GetBounds(b)
self.segmentation = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLLabelMapVolumeNode')
self.segmentation.SetName('seg'+str(deg))
self.padL = int(np.ceil((510 - self.npImage.shape[0]) / 2)) - np.mod(self.npImage.shape[0], 2)
self.padR = int(np.ceil((510 - self.npImage.shape[0]) / 2))
self.padU = int(np.ceil((788 - self.npImage.shape[1]) / 2)) - np.mod(self.npImage.shape[1], 2)
self.padD = int(np.ceil((788 - self.npImage.shape[1]) / 2))
self.newIm = np.pad(self.npImage, ((self.padL, self.padR), (self.padU, self.padD)), mode='constant')
self.newIm = img_as_float(self.newIm)
self.Immean = np.mean(self.newIm)
self.Imstd = np.std(self.newIm)
self.newIm -= self.Immean
self.newIm /= self.Imstd
rows = cols = 256
rimg = resize(self.newIm, (rows, cols), preserve_range=True)
rimgs = np.expand_dims(rimg, axis=0)
rimgs = np.expand_dims(rimgs, axis=3)
out = self.UNetModel.predict(rimgs)
self.o = np.squeeze(out)
self.mskout = resize(self.o, (510, 788), preserve_range=True)
slicer.util.updateVolumeFromArray(self.segmentation, np.expand_dims(self.mskout, axis=0)) #Generating the label map
self.shift.SetElement(0, 3, - self.padU)
self.shift.SetElement(1, 3, - self.padL)
self.shiftTransform.SetMatrixTransformToParent(self.shift)
self.shiftTransform.SetName('Shift')
mat = vtk.vtkMatrix4x4()
outputNode.GetIJKToRASMatrix(mat)
self.ijktoRasTransform.SetMatrixTransformToParent(mat)
self.ijktoRasTransform.SetAndObserveTransformNodeID(sliceToRasNode.GetID())
self.shiftTransform.SetAndObserveTransformNodeID(self.ijktoRasTransform.GetID())
self.segmentation.SetAndObserveTransformNodeID(self.shiftTransform.GetID())
self.segmentation.HardenTransform()
self.segLog.ImportLabelmapToSegmentationNode(self.segmentation, self.prostateSeg)
self.segLog.ExportAllSegmentsToModels(self.prostateSeg, 0)
Mods = slicer.mrmlScene.GetNodesByClassByName('vtkMRMLModelNode', 'seg'+str(deg))
self.segMod = Mods.GetItemAsObject(0)
polydata = self.segMod.GetPolyData()
self.APD.AddInputData(polydata)
self.APD.Update()
slicer.mrmlScene.RemoveNode(self.segMod)
slicer.mrmlScene.RemoveNode(self.segmentation)
self.prostateSeg.RemoveSegment('seg'+str(deg))
# combine all segmented slices to reconstruct the whole prostate gland
def performRecon(self):
outputNode = slicer.mrmlScene.GetFirstNodeByName("OutputVolume")
sliceToRasNode = slicer.mrmlScene.GetFirstNodeByName("SliceToRas")
for i in range(0,195,15):
self.applyRotation(outputNode,i,sliceToRasNode)
self.getSegmentationFromSlice(outputNode, sliceToRasNode,i)
self.onModelButtonClicked()
slicer.mrmlScene.RemoveNode(self.ijktoRasTransform)
slicer.mrmlScene.RemoveNode(self.shiftTransform)
slicer.mrmlScene.RemoveNode(outputNode)
slicer.mrmlScene.RemoveNode(sliceToRasNode)
slicer.mrmlScene.RemoveNode(self.prostateSeg)
self.ui.registerButton.enabled=True
'''Method to produce the reconstructed prostate model'''
def onModelButtonClicked(self):
out = self.APD.GetOutput()
self.edge.SetInputData(out)
self.edge.Update()
depth = 3
bound = self.edge.GetOutput()
normals = bound.GetPointData().GetNormals()
pts = bound.GetPoints().GetData()
pts_np = vtk.util.numpy_support.vtk_to_numpy(pts)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(pts_np)
pcd.normals = o3d.utility.Vector3dVector(normals)
poisson_mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=depth, width=0, scale=1.1, linear_fit=False)[0]
outputPath = self.path + './/Resources//Models//'
poisson_mesh = o3d.geometry.TriangleMesh.compute_triangle_normals(poisson_mesh)
o3d.io.write_triangle_mesh(outputPath + "mesh.stl", poisson_mesh)
self.finalModel = slicer.util.loadModel(outputPath+"mesh.stl")
os.remove(outputPath+"mesh.stl")
self.finalModel.SetName('Reconstructed Prostate')
Tnode = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLLinearTransformNode')
rot = vtk.vtkTransform()
rot.RotateX(-180)
rot.RotateY(-180)
Tnode.SetAndObserveTransformToParent(rot)
self.finalModel.SetAndObserveTransformNodeID(Tnode.GetID())
self.finalModel.HardenTransform()
slicer.mrmlScene.RemoveNode(Tnode)
DN = self.finalModel.GetDisplayNode()
DN.SetSliceIntersectionVisibility(1)
slicer.app.layoutManager().sliceWidget('Red').sliceLogic().GetSliceNode().SetSliceVisible(True)
slicer.app.layoutManager().setLayout(slicer.vtkMRMLLayoutNode.SlicerLayoutFourUpView)
'''Method to get quantitative metrics between a GT model and a predicted model'''
def metrics(self):
Predictedmodel = self.predNode
GroundTruthmodel = self.GTNode
#Generate Label map
Im = self.inputVolume
A = slicer.util.arrayFromVolume(Im)
# get the labelmap then array from the predicted model
seg = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLSegmentationNode')
segLog = slicer.modules.segmentations.logic()
seg.SetReferenceImageGeometryParameterFromVolumeNode(Im)
segLog.ImportModelToSegmentationNode(Predictedmodel,seg)
seg.SetReferenceImageGeometryParameterFromVolumeNode(Im)
LM = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLLabelMapVolumeNode')
segLog.ExportVisibleSegmentsToLabelmapNode(seg,LM,Im)
self.Predict = slicer.util.arrayFromVolume(LM)
Predict = self.Predict
# get the labelmap then array from the ground truth model
seg = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLSegmentationNode')
segLog = slicer.modules.segmentations.logic()
seg.SetReferenceImageGeometryParameterFromVolumeNode(Im)
segLog.ImportModelToSegmentationNode(GroundTruthmodel,seg)
seg.SetReferenceImageGeometryParameterFromVolumeNode(Im)
LM = slicer.mrmlScene.AddNewNodeByClass('vtkMRMLLabelMapVolumeNode')
segLog.ExportVisibleSegmentsToLabelmapNode(seg,LM,Im)
GT = slicer.util.arrayFromVolume(LM)
# compute the metrics based on TP, TN, FP, FN
self.true_pos = np.logical_and(GT==1,Predict==1)
self.true_neg = np.logical_and(GT==0,Predict==0)
self.false_pos = np.logical_and(GT==1,Predict==0)
self.false_neg = np.logical_and(GT==0,Predict==1)
self.true_pos = np.sum(self.true_pos)
self.true_neg = np.sum(self.true_neg)
self.false_pos = np.sum(self.false_pos)
self.false_neg= np.sum(self.false_neg)
self.recall = self.true_pos / (self.true_pos + self.false_neg)
self.specificity = self.true_neg / (self.true_neg + self.false_pos)
self.precision = self.true_pos / (self.true_pos + self.false_pos)
intersection = np.logical_and(GT,Predict)
# compute dice score
self.dice_score = 2.*intersection.sum()/(GT.sum()+Predict.sum())
# display the stats in the python interpreter
print("Dice:", self.dice_score)
print("Recall:", self.recall)
print("Specificity:", self.specificity)
print("Precision:", self.precision)