PyRadiomics

PyRadiomics is an open-source Python package that enables the standardized extraction of radiomic features from medical imaging data. It is designed to address the challenges in radiomics research and clinical adoption, such as the lack of standardized methodology, universal lexicon, and detailed feature extraction information.

Key features of PyRadiomics include:

1. Standardized feature extraction: PyRadiomics provides a set of standardized radiomic feature definitions and extraction methods, ensuring consistency across different studies and institutions.

2. Extensibility: The package allows users to customize and extend the feature extraction pipeline to suit their needs.

3. Interoperability: PyRadiomics supports various medical imaging formats, such as DICOM, NRRD, and NIfTI, making it compatible with various imaging data.

4. Computational efficiency: The package is optimized for efficient computation, enabling the processing of large datasets in a reasonable timeframe.

5. Documentation and examples: PyRadiomics provides extensive documentation and examples to help users get started and understand the package's functionality.

Topic

Medical imaging;Ontology and terminology;Genotype and phenotype;Oncology;Workflows

Detail

  • Operation: Image analysis;Parsing;Standardisation and normalisation

  • Software interface: Command-line user interface

  • Language: Python

  • License: BSD 3-Clause "New" or "Revised" License

  • Cost: Free of charge with restrictions

  • Version name: 3.1.0

  • Credit: The European Union's Horizon 2020 research and innovation program, the NIHR i4i Programme, the Cancer Research UK Grand Challenge Award.

  • Input: -

  • Output: -

  • Contact: Zhenwei Shi zhenwei.shi@maastro.nl

  • Collection: -

  • Maturity: Mature

Publications

  • Technical Note: Ontology-guided radiomics analysis workflow (O-RAW).
  • Shi Z, et al. Technical Note: Ontology-guided radiomics analysis workflow (O-RAW). Technical Note: Ontology-guided radiomics analysis workflow (O-RAW). 2019; 46:5677-5684. doi: 10.1002/mp.13844
  • https://doi.org/10.1002/MP.13844
  • PMID: 31580484
  • PMC: PMC6916323

Download and documentation


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