mirp 2.3.0

A package for standardised processing of medical imaging and computation of quantitative features.

Medical Image Radiomics Processor

MIRP is a python package for quantitative analysis of medical images. It focuses on processing images for integration with radiomics workflows. These workflows either use quantitative features computed using MIRP, or directly use MIRP to process images as input for neural networks and other deep learning models.

MIRP offers the following main functionality:

• Extract and collect metadata from medical images.
• Find and collect labels or names of regions of interest from image segmentations.
• Compute quantitative features from regions of interest in medical images.
• Process images for deep learning.

Tutorials

We currently offer the following tutorials:

• Computing quantitative features from MR images
• Applying filters to images

Documentation

Documentation can be found here: https://oncoray.github.io/mirp/

Supported Python and OS

MIRP currently supports the following Python versions and operating systems:

Python	Linux	Win	OSX
3.10	Supported	Supported	Supported
3.11	Supported	Supported	Supported
3.12	Supported	Supported	Supported

Supported imaging and mask modalities

MIRP currently supports the following image modalities:

File format	File type	Supported modality
DICOM	image	CT, MR (incl. ADC, DCE), PT, RTDOSE, CR, DX, MG
DICOM	mask	RTSTRUCT, SEG
NIfTI	any	any
NRRD	any	any
numpy	any	any

NIfTI, NRRD, and numpy files support any kind of (single-channel) image. MIRP cannot process RGB or 4D images.

Installing MIRP

MIRP is available from PyPI and can be installed using pip, or other installer tools:

pip install mirp

Examples - Computing Radiomics Features

MIRP can be used to compute quantitative features from regions of interest in images in an IBSI-compliant manner using a standardized workflow This requires both images and masks. MIRP can process DICOM, NIfTI, NRRD and numpy images. Masks are DICOM radiotherapy structure sets (RTSTRUCT), DICOM segmentation (SEG) or volumetric data with integer labels (e.g. 1, 2, etc.).

Below is a minimal working example for extracting features from a single image file and its mask.

from mirp import extract_features

feature_data = extract_features(
    image="path to image",
    mask="path to mask",
    base_discretisation_method="fixed_bin_number",
    base_discretisation_n_bins=32
)

Instead of providing the path to the image ("path_to_image"), a numpy image can be provided, and the same goes for "path to mask". The disadvantage of doing so is that voxel spacing cannot be determined.

MIRP also supports processing images and masks for multiple samples (e.g., patients). The syntax is much the same, but depending on the file type and directory structure, additional arguments need to be specified. For example, assume that files are organised in subfolders for each sample, i.e. main_folder / sample_name / subfolder. The minimal working example is then:

from mirp import extract_features

feature_data = extract_features(
    image="path to main image directory",
    mask="path to main mask directory",
    image_sub_folder="image subdirectory structure relative to main image directory",
    mask_sub_folder="mask subdirectory structure relative to main mask directory",
    base_discretisation_method="fixed_bin_number",
    base_discretisation_n_bins=32
)

The above example will compute features sequentially. MIRP supports parallel processing using the ray package. Feature computation can be parallelized by specifying the num_cpus argument, e.g. num_cpus=2 for two CPU threads.

Examples - Image Preprocessing for Deep Learning

Deep learning-based radiomics is an alternative to using predefined quantitative features. MIRP supports preprocessing of images and masks using the same standardized workflow that is used for computing features.

Below is a minimal working example for preprocessing deep learning images. Note that MIRP uses the numpy notation for indexing, i.e. indices are ordered [z, y, x].

from mirp import deep_learning_preprocessing

processed_images = deep_learning_preprocessing(
    image="path to image",
    mask="path to mask",
    crop_size=[50, 224, 224]
)

Examples - Summarising Image Metadata

MIRP can also summarise image metadata. This is particularly relevant for DICOM files that have considerable metadata. Other files, e.g. NIfTI, only have metadata related to position and spacing of the image.

Below is a minimal working example for extracting metadata from a single image file.

from mirp import extract_image_parameters

image_parameters = extract_image_parameters(
    image="path to image"
)

MIRP also supports extracting metadata from multiple files. For example, assume that files are organised in subfolders for each sample, i.e. main_folder / sample_name / subfolder. The minimal working example is then:

from mirp import extract_image_parameters

image_parameters = extract_image_parameters(
    image="path to main image directory",
    image_sub_folder="image subdirectory structure relative to main image directory"
)

Examples - Finding labels

MIRP can identify which labels are present in masks. For a single mask file, labels can be retrieved as follows:

from mirp import extract_mask_labels

mask_labels = extract_mask_labels(
    mask="path to mask"
)

MIRP supports extracting labels from multiple masks. For example, assume that files are organised in subfolders for each sample, i.e. main_folder / sample_name / subfolder. The minimal working example is then:

from mirp import extract_mask_labels
mask_labels = extract_mask_labels(
    mask="path to main mask directory",
    mask_sub_folder="mask subdirectory structure relative to main mask directory"
)

Transitioning to version 2

Version 2 is a major refactoring of the previous code base. For users this brings the following noticeable changes:

• MIRP was previously configured using two xml files: config_data.xml for configuring directories, data to be read, etc., and config_settings.xml for configuring experiments. While these two files can still be used, MIRP can now be configured directly, without using these files.
• The main functions of MIRP (mainFunctions.py) have all been re-implemented.
  • mainFunctions.extract_features is now extract_features (functional form) or extract_features_generator (generator). The replacements allow for both writing feature values to a directory and returning them as function output.
  • mainFunctions.extract_images_to_nifti is now extract_images (functional form) or extract_images_generator (generator). The replacements allow for both writing images to a directory (e.g., in NIfTI or numpy format) and returning them as function output.
  • mainFunctions.extract_images_for_deep_learning has been replaced by deep_learning_preprocessing (functional form) and deep_learning_preprocessing_generator (generator).
  • mainFunctions.get_file_structure_parameters and mainFunctions.parse_file_structure are deprecated, as the the file import system used in version 2 no longer requires a rigid directory structure.
  • mainFunctions.get_roi_labels is now extract_mask_labels.
  • mainFunctions.get_image_acquisition_parameters is now extract_image_parameters.

For advanced users and developers, the following changes are relevant:

• MIRP previously relied on ImageClass and RoiClass objects. These have been completely replaced by GenericImage (and its subclasses, e.g. CTImage) and BaseMask objects, respectively. New image modalities can be added as subclass of GenericImage in the mirp.images submodule.
• File import, e.g. from DICOM or NIfTI files, in version 1 was implemented in an ad-hoc manner, and required a rigid directory structure. Since version 2, file import is implemented using an object-oriented approach, and directory structures are more flexible. File import of new modalities can be implemented as a relevant subclass of ImageFile.
• MIRP now uses the ray package for parallel processing.

Citation info

A publication for MIRP is forthcoming. For now, please cite the following work: Zwanenburg A, Leger S, Agolli L, Pilz K, Troost EG, Richter C, Löck S. Assessing robustness of radiomic features by image perturbation. Scientific reports. 2019 Jan 24;9(1):614.

Contributing

If you have ideas for improving MIRP, please read the short contribution guide.

Developers and contributors

MIRP is developed by:

• Alex Zwanenburg

We would like thank the following contributors:

• Stefan Leger
• Sebastian Starke