Introduction
What is radiomics?
Radiomics is the process of using medical images for, e.g., diagnosing disease or predicting how patients respond to a treatment. Radiomics involves the use of computer algorithms to process medical images and predicting such outcomes. There are two major radiomics branches, defined by what algorithms are used to predict outcomes. The first branch (historically) of radiomics is characterised by the use of quantitative (handcrafted) features that are then used by machine learning algorithms for tabular data. The second, more recent, branch uses deep learning algorithms to directly learn from images themselves.
For more details, see reviews by Lambin et al. [Lambin2017] and van Timmeren et al. [vanTimmeren2020].
What is MIRP?
Medical Image Radiomics Processor (MIRP) is a python package for medical image analysis that is compliant with the reference standards of the Image Biomarker Standardisation Initiative (IBSI) [Zwanenburg2020], [Whybra2024]. MIRP focuses on radiomics applications and supports computation of features for conventional radiomics and image processing for deep-learning applications.
Why MIRP?
In radiomics, image processing and feature computation are part of a larger workflow that also includes machine learning. Python has some of the most commonly used machine learning packages, such scikit-learn and pytorch. However, there was no Python package for image processing and feature computation that was fully compliant with the IBSI reference standards – i.e. a package whose output is reproducible by other IBSI-compliant software. MIRP fills this gap.
Contact
If you have any questions or run into issues, please visit the MIRP GitHub repository.
References
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14: 749-762. doi:10.1038/nrclinonc.2017.141
van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-“how-to” guide and critical reflection. Insights Imaging. 2020;11: 91. doi:10.1186/s13244-020-00887-2
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
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