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dicom2stl

CircleCI Binder Python application

Tutorial: Binder

dicom2stl.py is a script that takes a Dicom series and generates a STL surface mesh.

Written by David T. Chen from the National Institute of Allergy & Infectious Diseases (NIAID), dchen@mail.nih.gov It is covered by the Apache License, Version 2.0:

http://www.apache.org/licenses/LICENSE-2.0

Required packages

The script is written in Python and uses 2 external packages, VTK and SimpleITK.

vtk can be downloaded and built from the following repository:

https://github.com/Kitware/VTK

Alternatively, on some Linux distributions it can be installed with the following command:

sudo apt-get install vtk

SimpleITK can be installed via the following command:

pip install SimpleITK

The options for the script can be seen by running it:

python dicom2stl.py --help

How it works

First the script reads in a series of 2-d images or a simple 3-d image. It can read any format supported by ITK. If the input name is a zip file or a directory name, the script expects a single series of DCM images, all with the ".dcm" suffix.

Note: if you run this script with the individual Dicom slices provided on the command line, they might not be ordered in the correct order. You are better off providing a zip file or a directory. Dicom slices are not necessarily ordered the same alphabetically as they are physically. In the case of a zip file or directory, the script loads using the SimpleITK ImageSeriesReader class, which orders the slices by their physical layout, not their alphabetical names.

The primary image processing pipeline is as follows:

The script has built in double threshold values for the 4 different tissue types (bone, skin, muscle, soft). These values assume the input is DICOM with standard CT Hounsfield units. I determined these values experimentally on a few DICOM test sets I had, so how well they work for others is in question.

The volume is shrunk to 256 cubed or less for speed and polygon count reasons.

After all the image processing is finished, the volume is converted to a VTK image using sitk2vtk.py.

Then the following VTK pipeline is executed:

The amount of smoothing and mesh reduction can be adjusted via command line options. By default 25 iterations of smoothing is applied and the number of vertices is reduced by 90%.

Examples

To extract the bone from a zip of dicom images:

python dicom2stl.py -t bone -o bone.stl dicom.zip

To extract the skin from a NRRD volume:

python dicom2stl.py -t skin -o skin.stl volume.nrrd

To extract a specific iso-value from a VTK volume:

python dicom2stl.py -i 128 -o iso.stl volume.vtk

To extract soft tissue from a dicom series in directory and apply a 180 degree Y axis rotation:

python dicom2stl.py --enable rotation -t soft_tissue -o soft.stl dicom_dir

You can try out an interactive Jupyter notebook via Binder: Binder

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Python script to extract a STL surface from a DICOM image series.

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