Skip to content
Switch branches/tags

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

Repeatability of Multiparametric Prostate MRI Radiomics Features

This repository contains data and code accompanying our publication on "Repeatability of Multiparametric Prostate MRI Radiomics Features"

Schwier, M., van Griethuysen, J., Vangel, M. G., Pieper, S., Peled, S., Tempany, C., Aerts, H. J. W. L., Kikinis, R., Fennessy, F. M. & Fedorov, A. Repeatability of Multiparametric Prostate MRI Radiomics Features. Sci. Rep. 9, 9441 (2019).


NOTE Some specific adaptions for the figure generations have been done for the Nature Scientific Reports version. You can find these in the "ScientificReportsRevision*" branches.


Extracted Features

The EvalData folder contains all extracted features we used in the study.

File Format Description

The files are in CSV format. Each row contains all features extracted for one image and mask combination. The organization and naming of the columns is directly taken from pyradiomics. We just added a few columns containing some additional meta information about the image/mask from which the features were derived.

The first few columns contain general info about the feature extraction (prefixed with "general_info"):

Column Name (w/o prefix) Meaning
BoundingBox The bounding box considered around the mask
EnabledImageTypes Indicates which pre-filtering options were activated for the extraction
GeneralSettings Indicates which general settings were activated for the extraction (e.g. normalization, resampling, ...)
ImageHash Unique identifier of the image
ImageSpacing Voxel spacing
MaskHash Unique identifier of the mask
NumpyVersion Numpy version used by pyradiomics
PyWaveletVersion PyWavelet version used by pyradiomics
SimpleITKVersion SimpleITK version used by pyradiomics
Version Pyradiomics version used for the extraction
VolumeNum Number of zones (connected components) within the mask for the specified label
VoxelNum Number of voxels in the mask

Afterwards follow the columns for each feature. The feature column names follow this pattern:

[pre-filter]_[feature group]_[feature name]

For example:

  • original_shape_Volume
  • wavelet-HH_glcm_JointEnergy

At the end we added a few columns with additional meta information:

Column Name Meaning
study Identifying the study this image belongs to
series Identifying the series this image belongs to
canonicalType Type/modality of the image (e.g. ADC, T2w, SUB)
segmentedStructure Type of structure segmented by the mask

Filename Pattern Description

The filenames of the feature data CSVs also contain some additional meta information about their content. The jupyter notebook for generating figures will parse this information and save it with the statistics it creates from the feature data (so you don't really have to worry about these too much).

The following table explains the different "codes" in the filename of a feature CSV file:

File name contains Meaning
FullStudySettings Simply indicates that the extraction settings were according to this study (we also did smaller studies)
noNormalization Indicates that the default pyradiomics whole-image normalization was deactivated
2d/3D Indicates if texture features were computed in 2D or 3D
biasCorrected Indicates that we applied bias correction to the T2w images before processing them with pyradiomics
TP2Registered Indicates that for the T2w images we didn't use the first manual segmentation but used registration to transfer the second timepoint masks to the first timepoint (see paper for more info)
MuscleRefNorm Indicates that we normalized the T2w images against a consistent reference region in muscle tissue
T2AX Contains only results for T2w images
bin10/bin15/bin20/bin40 Bin size used for texture feature computation

Jupyter Notebooks

The jupyter notebooks require Python >= 3.6.

Figure generation and data analysis

The FullStudy_RepeatabilityStudy.ipynb contains the code that was used to generate the figures for the pre-print paper. It also contains additional figures and can be used as a good basis to further analyze the data and create your own additional figures.

This notebook should run out-of-the-box, if you have the whole repository cloned and all Python dependencies installed (see imports in notebook). The generated figures will be saved into the EvalData/plots folder.

Feature extraction

Re-running the feature extraction (i.e. re-creating the files you find in the EvalData folder) does not work out-of-the-box. You will need to request access and download the QIN-PROSTATE-Repeatability TCIA collection (check the paper for details). You can then use 3D Slicer to convert the data into "mpReview" style data. This is the structure we used for running the extraction with pyradiomics. You can use the mpReviewPreprocessor converter utility of the 3D Slicer Multiparametric Review (mpReview) module to convert the TCIA data into the required format (see the mpReview readme). However, as noted in the paper, we also performed additional processing to create variations of the data, like bias correction and registration. Please see the paper for details (all non-pyradiomics pre-processing was done with 3D Slicer).

The FullStudy_ExtractPyRadiomics.ipynb should give you an idea how we performed the feature extraction. You will also need the pyradiomics library. Note that it is not guaranteed that a different version of pyradiomics will create the same results for all features. Check the paper as well as the feature data files for the version that was used for this study. The basic settings for pyradiomics feature extractions, as used in the jupyter notebook, are located in the PyRadiomicsSettings folder.

The FullStudy_ExtractPyRadiomics.ipynb notebook requires a custom Python library, which you should download and put in a location where it will be found by Python (e.g. point the PYTHONPATH environment variable to the parent folder):

  • mpReviewUtils (only required for the feature extraction notebook)