Normally aorta segmentation on contrasted CT image should be straightforward: simple local thresholding/region growing methods should work. For example, you can use “Local threshold” Segment Editor effect in 3D Slicer to segment aorta by a single click, as shown here:
https://www.youtube.com/watch?v=yi07mjr3JeU
There are a few more aorta segmentation recipes here:
https://lassoan.github.io/SlicerSegmentationRecipes/
Andras
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Normally aorta segmentation on contrasted CT image should be straightforward: simple local thresholding/region growing methods should work. For example, you can use “Local threshold” Segment Editor effect in 3D Slicer to segment aorta by a single click, as shown here:
https://www.youtube.com/watch?v=yi07mjr3JeU
There are a few more aorta segmentation recipes here:
https://lassoan.github.io/SlicerSegmentationRecipes/
Andras
From: vmtk-...@googlegroups.com <vmtk-...@googlegroups.com> On Behalf Of Jessica Williams
Sent: Thursday, June 18, 2020 7:40 PM
To: vmtk-users <vmtk-...@googlegroups.com>
Subject: [vmtk-users] Full Aorta Segmentation
I was wondering how best to go about segmenting the aorta with vmtk. I've tried the vmtklevelsetsegmentation with the "colliding fronts" algorithm and got the attached image joining two branches (the descending aorta + the aortic arch).
I've noticed the surface is not smooth, and there are, in fact some holes. What would you recommend for me to improve this? I'd also like to automate the process as much as possible - where are the best locations within the aorta for seed points to be placed?
Any help would be very much appreciated.
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Yes, you can access all features of 3D Slicer from Python (and you can install any Python packages inside 3D Slicer), so you can implement your complete processing workflow in one Python environment. You can find a couple of examples here: https://www.slicer.org/wiki/Documentation/Nightly/ScriptRepository#How_to_run_segment_editor_effects_from_a_script
In case of aorta segmentation, segmentation takes about 30 seconds (adjust threshold value, if needed; then click in aorta) and then you need to check the quality of your segmentations anyway. Considering, that fully automating the segmentation, would probably take a few hours for an experienced Slicer, but may take 1-2 days for a newcomer, the time that you invest into automation would only pay off if you segment several hundreds of images. However, if you already segmented that many images then you could use those to train a CNN to automate the segmentation, and nowadays an AI-based segmentation would sell your research or product better even if its performance is not better than a “classic” method, such as threshold-masked growcut.
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You can load a volume from a .vti file into 3D Slicer using the script below (and then you can process it or save it to nrrd or other format): https://www.slicer.org/wiki/Documentation/Nightly/ScriptRepository#Load_volume_from_.vti_file
All VMTK is available in Slicer’s Python environment after you install SlicerVMTK extension. So, you can run your VMTK Python scripts directly in Slicer. The advantage of this approach is that you can implement your complete processing workflow (user interaction, visualization, computation, etc.) in one place. You can also use Slicer to run scripts in batch mode (without GUI) or if you prefer to run Python processing in Jupyter notebooks, you can run Slicer as a Jupyter kernel (https://github.com/Slicer/SlicerJupyter#slicerjupyter).
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Slicer includes VMTK (if you install SlicerVMTK extension), so you don’t need to choose between VMTK and Slicer. You can specify seed points from Python script or from GUI.
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