MIRP

Welcome to MIRP! If you need to quantitatively analyse medical images using a standardised workflow, you have come to the right place!

What can MIRP help you do?

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.

Supported image and mask modalities

Data format	Data 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
MIRP-native	any	any

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

Supported Python versions and operating systems

Python	Linux	Windows	macOS
3.10	✅	✅	✅
3.11	✅	✅	✅
3.12	✅	✅	✅
3.13	✅	✅	✅

Optional dependencies for parallel processing, i.e. ray and joblib, may not support all Python versions.

Compatibility

MIRP is compliant with the Image Biomarker Standardisation Initiative’s reference standards for image processing and feature computation [Zwanenburg2020] and for image filters [Whybra2024]. Compliance is checked automatically as part of the software tests.

The reference standards do not cover all possible image pre-processing steps, features or image filters. There are three pre-processing steps in MIRP that are used by default and that currently lack reference standards, and thus may be implemented differently in other software packages, or be absent entirely. These are:

1. Anti-aliasing: Downsampling, i.e. resampling from a high-resolution scan to a lower-resolution image leads to aliasing artifacts because the interpolation algorithms will primary use local information from the voxels directly adjacent to each interpolation point [Zwanenburg2019]. To counteract this, MIRP by default uses a Gaussian anti-aliasing filter prior to resampling. This filters smooths local information, prevent anti-aliasing artifacts. To turn anti-aliasing off set anti_aliasing = False.

2. Use of tissue masks for intensity normalisation: Intensity normalisation is a pre-processing step commonly used for harmonising clinical MRI sequences. These methods use information from intensities in a scan. Generally, information from voxels outside the studied patient (air voxels) are not relevant. By default, MIRP tries to exclude such voxels by generating a tissue mask based on the intensity distribution, and use only information from voxels related containing patient tissue for normalisation. To turn tissue masks off, set tissue_mask_type = “none”.

3. Conversion of PET values to SUV: If PET scans are provided in the DICOM format, by default MIRP will use information contained in the DICOM headers to compute body-weight corrected standardised uptake values. To use raw PET information, set pet_suv_conversion = “none”. Other types of standardised uptake value can be computed using other options.

References

[Zwanenburg2019]

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;9(1):614. doi: 10.1038/s41598-018-36938-4

[Zwanenburg2020]

Zwanenburg A, Vallieres M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020;295: 328-338. doi:10.1148/radiol.2020191145

[Whybra2024]

Whybra P, Zwanenburg A, Andrearczyk V, Schaer R, Apte AP, Ayotte A, et al. The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights. Radiology. 2024;310: e231319. doi:10.1148/radiol.231319