Package: pyAutoRef
pyAutoRefThis is the python version of the
"Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition"This is an automated method for dual-reference tissue (fat and muscle) normalization of T2-weighted MRI for the prostate.The method was developed at the CIMORe group at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway.
[https://www.ntnu.edu/isb/cimore]For detailed information about this method, please read our paper: [https://link.springer.com/article/10.1007%2Fs10334-020-00871-3]NoteThe provided algorithm was developed for research use and was NOT meant to be used in clinic.StructurepyAutoRef/
├── LICENSE
├── pyproject.toml
├── README.md
├── setup.cfg
├── src/
│   └── pyAutoRef/
│       ├── __init__.py
│       ├── autoref.py
│       ├── pre_processing.py
│       ├── object_detection.py
│       ├── post_processing.py
│       ├── normalization.py
│       ├── utils.py
│       ├── MANIFEST.in
│       └── model.onnx
└── tests/InstallationYou can install the package either from pip or using pip or the files in GitHub repository [https://github.com/MohammedSunoqrot/pyAutoRef]pipSimply type:pip install pyAutoRefGitHubClone the GitHub repositoryFrom command linegit clone https://github.com/MohammedSunoqrot/pyAutoRef.gitChange directory to the clones folder (unzip if needed) and typepip install .Difference NoteThis python version is differ than the originally published MATLAB version [https://github.com/ntnu-mr-cancer/AutoRef] in terms:It accepts all kind of SimpleITK supported image format.It uses YOLOv8 model for object detector instead of ACF.
(This model is trained using the same data and bounding boxes used to train ACF in the original/MATLAB code).How to cite AutoRef/pyAutoRefIn case of using or refering to AutoRef/pyAutoRef, please cite it as:Sunoqrot, M.R.S., Nketiah, G.A., Selnæs, K.M. et al. Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition. Magn Reson Mater Phy 34, 309–321 (2021). [https://doi.org/10.1007/s10334-020-00871-3]How to use pyAutoRefTo perform AutoRef normalization, you first need to import theautoreffunction.
You can do it by callingfrom pyAutoRef import autorefautorefFunctionParameters:
input_image_path (str): The file path to the input 3D image (any supported SimpleITK format) or to the DICOM folder.
output_image_path (str, optional): The file path to save the normalized output image to any supported SimpleITK format.
If None, the image will not be saved.Returns:
normalized_image (SimpleITK.Image): The normalized 3D image.Supported input/output formatsDICOM Series.All the medicalimages formats supported by SimpleITK.[SimpleITK.Image] (https://simpleitk.org/SimpleITK-Notebooks/01_Image_Basics.html).DICOM Series is recognized when there is no file extensionExamples of usage:Example (input: medical image format, output: medical image format):from pyAutoRef import autoref

input_image_path = r"C:\Data\Case10_t2.nii.gz"
output_image_path = r"C:\Data\Case10_t2_normalized.nii.gz"

autoref(input_image_path, output_image_path)Example (input: medical image format, output: DICOM Series):from pyAutoRef import autoref

input_image_path = r"C:\Data\Case10_t2.nii.gz"
output_image_path = r"C:\Data\Case10_t2_normalized"

autoref(input_image_path, output_image_path)Example (input: DICOM Series, output: medical image format):from pyAutoRef import autoref

input_image_path = r"C:\Data\Case10_t2"
output_image_path = r"C:\Data\Case10_t2_normalized.nii.gz"

autoref(input_image_path, output_image_path)Example (input: DICOM Series, output: DICOM Series):from pyAutoRef import autoref

input_image_path = r"C:\Data\Case10_t2"
output_image_path = r"C:\Data\Case10_t2_normalized"

autoref(input_image_path, output_image_path)