Colour Gradient Based on Pixel Density of Overlaid Segmentations?


I am new to 3D slicer, but I have a series of MRIs which I have overlaid and segmented. I would like to now take those segmentations and apply a filter to visualize their overlap. I have seen this done with two segmentations, but as I have ~40 I would like to use a colour scale to express the relative amount of overlap.

Any suggestions would be appreciated!

Thanks, Greg

A simple way of dealing with this is to write a short Python script, which does the following:

  • create an “accumulation volume” (segments from all images will be added to it) fill with all zeros, get it as a numpy array (using slicer.util.arrayFromVolume())
  • load each segmentation, export the segment to a numpy array (using slicer.util.arrayFromSegmentBinaryLabelmap), you threshold this numpy array (set it to 1 where it has non-zero values), and add the values to the accumulation volume’s numpy array
  • update the accumulation volume node from its numpy array (using slicer.util.arrayFromVolumeModified())

Hi Andras,

Thank you for your response. Sorry I am still a little confused, I believe I have followed your instructions (although I am very much still learning so the mistake is probably mine), but now I just see a complete overlay of all these segmentations. Is there a way to apply a colour gradient based on the relative overlap?

Thanks, Greg

Could you copy the otthon script that you created? We could tell you what to add or change to see the result as a scalar volume instead of a binary.

Hi Andras,

I am not terribly familiar with python (so after a few failed attempts) I used the method you mention under: Segment / BinaryLabelMap to numpy array.

Would this work for the problem?

You need to add the numpy arrays so that if a voxel is set to 1 in multiple segmentations then that voxel value will be larger than 1 (the value should be the number of segmentations where that voxel is non-zero).

Hi Andras,

I think I must be making some mistake, when I try to make an accumulation volume using the code above I get thrown:

File “”, line 1, in
File “/Applications/”, line 1476, in arrayFromVolume
vimage = volumeNode.GetImageData()
AttributeError: ‘str’ object has no attribute ‘GetImageData’


arrayFromVolumeNode takes a volume node object as input (not the volume’s name). You can get the volume node object from the volume’s name by calling:

volumeNode = slicer.util.getNode('MyVolumeName')

Hi Andras,

When entering this code I run into the following problem:
Traceback (most recent call last):
File “”, line 1, in
File “/Applications/”, line 1324, in getNode
raise MRMLNodeNotFoundException(“could not find nodes in the scene by name or id ‘%s’” % (pattern if (isinstance(pattern, str)) else “”))
slicer.util.MRMLNodeNotFoundException: could not find nodes in the scene by name or id ‘the_scene_I_inputted.mrml’.

Sorry for all of these small errors

Replace MyVolumeName with the actual name of your volume node. It seems that the string that the method got was the_scene_I_inputted.mrml which does not seem like a volume node name.

Hi Andras, sorry I should have clarified.

I replaced ‘My Volume Name’ with the volume of the scene I was working with. The ‘the_scene_I_inputted.mrml’ is meant to be the placeholder for the scene that I inputted under ‘My Volume’. Sorry for the confusion.

This code should work. It produces a volume named ExportVolume, which you can use in Volume Rendering to assign colors and opacities to show the density of original segments in each location.

# Export one volume that shows the sum of segments in a segmentation of the same shape

# Update these names using your scene

segmentationNodeName = '2'
masterVolumeName = '10 ciss3d_tra_iso'
outputVolumeName = 'ExportVolume'

# Prepare nodes that will be needed

segmentationNode = slicer.mrmlScene.GetFirstNodeByName(segmentationNodeName)
segLogic = slicer.modules.segmentations.logic()
segmentation = segmentationNode.GetSegmentation()
labelmapVolume = slicer.mrmlScene.AddNewNodeByClass("vtkMRMLLabelMapVolumeNode")

# Make sure the output volume exists, and it has the same shape as the master volume for segmentation

outputVolume = slicer.mrmlScene.GetFirstNodeByName(outputVolumeName)
masterVolume = slicer.mrmlScene.GetFirstNodeByName(masterVolumeName)
if outputVolume is None:
  volumesLogic = slicer.modules.volumes.logic()
  outputVolume = volumesLogic.CloneVolume(slicer.mrmlScene, masterVolume, outputVolumeName)

# Prepare output volume array and set voxel values to zero

exportArray = slicer.util.arrayFromVolume(outputVolume)
exportArray[:] = 0

# Add each segment to the output array one-by-one

for i in range(segmentation.GetNumberOfSegments()):
  segmentIds = vtk.vtkStringArray()
  segmentId = segmentation.GetNthSegmentID(i)
  segLogic.ExportSegmentsToLabelmapNode(segmentationNode, segmentIds, labelmapVolume, masterVolume)
  labelArray = slicer.util.arrayFromVolume(labelmapVolume)
  exportArray = exportArray + labelArray

# Update the output volume

slicer.util.updateVolumeFromArray(outputVolume, exportArray)