Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research Journal Article


Authors: Apte, A. P.; Iyer, A.; Crispin-Ortuzar, M.; Pandya, R.; van Dijk, L. V.; Spezi, E.; Thor, M.; Um, H.; Veeraraghavan, H.; Oh, J. H.; Shukla-Dave, A.; Deasy, J. O.
Article Title: Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research
Abstract: Purpose: Radiomics is a growing field of image quantitation, but it lacks stable and high-quality software systems. We extended the capabilities of the Computational Environment for Radiological Research (CERR) to create a comprehensive, open-source, MATLAB-based software platform with an emphasis on reproducibility, speed, and clinical integration of radiomics research. Method: The radiomics tools in CERR were designed specifically to quantitate medical images in combination with CERR's core functionalities of radiological data import, transformation, management, image segmentation, and visualization. CERR allows for batch calculation and visualization of radiomics features, and provides a user-friendly data structure for radiomics metadata. All radiomics computations are vectorized for speed. Additionally, a test suite is provided for reconstruction and comparison with radiomics features computed using other software platforms such as the Insight Toolkit (ITK) and PyRadiomics. CERR was evaluated according to the standards defined by the Image Biomarker Standardization Initiative. CERR's radiomics feature calculation was integrated with the clinically used MIM software using its MATLAB® application programming interface. Results: The CERR provides a comprehensive computational platform for radiomics analysis. Matrix formulations for the compute-intensive Haralick texture resulted in speeds that are superior to the implementation in ITK 4.12. For an image discretized into 32 bins, CERR achieved a speedup of 3.5 times over ITK. The CERR test suite enabled the successful identification of programming errors as well as genuine differences in radiomics definitions and calculations across the software packages tested. Conclusion: The CERR's radiomics capabilities are comprehensive, open-source, and fast, making it an attractive platform for developing and exploring radiomics signatures across institutions. The ability to both choose from a wide variety of radiomics implementations and to integrate with a clinical workflow makes CERR useful for retrospective as well as prospective research analyses. © 2018 American Association of Physicists in Medicine
Keywords: reproducibility; imaging biomarker; machine learning; open source software; radiomics; inter-software test
Journal Title: Medical Physics
Volume: 45
Issue: 8
ISSN: 0094-2405
Publisher: American Association of Physicists in Medicine  
Date Published: 2018-08-01
Start Page: 3713
End Page: 3720
Language: English
DOI: 10.1002/mp.13046
PROVIDER: scopus
PUBMED: 29896896
PMCID: PMC6597320
DOI/URL:
Notes: Article -- Export Date: 4 September 2018 -- Source: Scopus
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MSK Authors
  1. Amita Dave
    127 Dave
  2. Jung Hun Oh
    175 Oh
  3. Joseph Owen Deasy
    488 Deasy
  4. Aditya Apte
    190 Apte
  5. Maria Elisabeth Thor
    135 Thor
  6. Aditi Iyer
    44 Iyer
  7. Hyemin Um
    13 Um
  8. Rutu Pandya
    3 Pandya