InnerEye-DICOM-RT contains tools to convert medical datasets in NIFTI format to DICOM-RT. Datasets converted using this tool can be consumed directly by InnerEye-DeepLearning.
Most of the work is done by a .NET Core 2.1 project in RTConvert, written in C#. There is a very lightweight wrapper around this so that it can be consumed from Python. The wrapper relies on the PyPI package https://pypi.org/project/dotnetcore2/ which wraps up .NET Core 2.1.
Get the installer from Git for Windows
The installer will prompt you to "Select Components". Make sure that you tick
- Git LFS (Large File Support)
- Git Credential Manager for Windows
After the installation, open a command prompt or the Git Bash:
- Run
git lfs install
to set up the hooks in git - Run
git config --global core.autocrlf true
to ensure that line endings are working as expected
Clone the InnerEye-DICOM-RT repository on your machine: Run git lfs clone --recursive https://github.com/microsoft/InnerEye-DICOM-RT
The C# components can be built with the .NET Core SDK. We use version 2.1 for compatibility with the PyPI package dotnetcore2
.
Installation instructions are here: https://docs.microsoft.com/en-us/dotnet/core/install/.
Visual Studio is not required to build, but if you wish to use it then for .Net Core 2.1 you need at least:
Visual Studio 2017 version 15.7.
RTConvert can be built from a .NET Core command line:
dotnet build RTConvert
There are unit tests:
dotnet test RTConvert
Note that the unit tests have a dependency on System.Drawing
and that on Linux System.Drawing
requires a native package:
apt-get -s install libgdiplus
Finally, for consumption by the Python wrapper, this solution must be published:
dotnet publish RTConvert --configuration Release -p:Platform=x64
This should create a folder with all the requirements for RTConvert at:
RTConvert/Microsoft.RTConvert.Console/bin/x64/Release/netcoreapp2.1/publish/*
Echo is a very simple application that takes 1 or 2 arguments. The first is echoed to stdout
, and if a
second argument is supplied then it is echoed to stderr
. This is only required for units tests to establish
that a .NET Core application can be called.
Echo can be built from a .NET Core command line:
dotnet build Echo
There are no unit tests.
Finally, for consumption by the Python wrapper, this solution must be published:
dotnet publish Echo --configuration Release -p:Platform=x64
This should create a folder with all the requirements for Echo at: Echo/Echo/bin/x64/Release/netcoreapp2.1/publish/*
The Python wrapper is in src/InnerEye_DICOM_RT/nifti_to_dicom_rt_converter.py
. It simply uses subprocess.Popen
to invoke
the .NET Core application passing in the relevant dll and command line arguments.
It does require that the RTConvert and Echo published packages are copied to the folder: src/InnerEye_DICOM_RT/bin/netcoreapp2.1
.
Note that the github build action does this automatically, but if testing then this needs to be done manually.
The Python package is created with:
python setup.py sdist bdist_wheel
which builds a source distribution and wheel to the dist
folder.
To run the Python tests:
pip install pytest dotnetcore2
pytest tests
To consume this package:
pip install InnerEye-DICOM-RT
To call RTConvert:
from InnerEye_DICOM_RT.nifti_to_dicom_rt_converter import rtconvert
(stdout, stderr) = rtconvert(
in_file=NiftiSegmentationLocation,
reference_series=DicomVolumeLocation,
out_file=OutputFile,
struct_names=StructureNames,
struct_colors=StructureColors,
fill_holes=FillHoles,
roi_interpreted_types=ROIInterpretedTypes,
manufacturer=Manufacturer,
interpreter=Interpreter,
modelId=ModelId
)
where:
in_file
is the path to the input Nifti file. This file is a 3D volume in Nifti format.reference_series
is the path to the input folder containing the reference DICOM series;out_file
is the path to the output DICOM-RT file;struct_names
is a list of structure names like: ["External", "parotid_l", "parotid_r", "smg_l"]. Each structure name corresponds to a non-zero voxel value in the input volume. In the example External corresponds to voxel value 1, parotid_l to 2, etc. Voxels with value 0 are dropped. If there are voxels without a corresponding structure name, they will also be dropped. The structure name will become its ROI Name in the Structure Set ROI Sequence in the Structure Set in the DICOM-RT file.struct_colors
is a list of structure colors in hexadecimal notation like: ["000000", "FF0080", "00FF00", "0000FF"]. Each color in this list corresponds to a structure in struct_names and will become its ROI Display Color in the ROI Contour Sequence in the ROI Contour in the DICOM-RT file. If there are less colors than struct_names, or if an entry is empty, the default is red (FF0000);fill_holes
is a list of bools like: [True, False, True]. If there are less bools than struct_names, or if an entry is empty, the default is false. If True then any contours found per slice will have their holes filled, otherwise contours will be returned as found.modelId
Model name and version from AzureML. E.g. Prostate:123manufacturer
Manufacturer for the DICOM-RT (check DICOM-RT documentation)interpreter
Interpreter for the DICOM-RT (check DICOM-RT documentation)roi_interpreted_types
is a list of ROIInterpretedType. Possible values (None, CTV, ORGAN, EXTERNAL).
You are responsible for the performance, the necessary testing, and if needed any regulatory clearance for any of the models produced by this toolbox.
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