CERR: Computational Environment for Radiological Research

CERR (pronounced ‘sir’), stands for Computational Environment for Radiological Research. CERR is MATLAB based software platform for developing and sharing research results using radiation therapy treatment planning and imaging informatics. CERR has a wide variety of dose, imaging, and structure analysis and comparison tools. CERR is open-source and GNU GPL copyrighted.

More info can be found in the following excerpts of CERR reference paper 1 & paper 2:

CERR partially addresses four broad needs in treatment planning research:

  • provides a convenient and powerful software environment to develop and prototype treatment planning concepts,
  • serves as a software integration environment to combine treatment planning software written in multiple languages (MATLAB, FORTRAN, C/C++, JAVA, etc.), together with treatment plan information (computed tomography scans, outlined structures, dose distributions, digital films, etc.),
  • provides the ability to extract treatment plans from disparate planning systems using the widely available AAPM/RTOG archiving mechanism, and
  • provides a convenient and powerful tool for sharing and reproducing treatment planning research results.

The functional components currently being distributed, including source code, include:

  • an import program which converts the widely available AAPM/RTOG treatment planning format into a MATLAB cell‐array data object, facilitating manipulation;
  • viewers which display axial, coronal, and sagittal computed tomography images, structure contours, digital films, and isodose lines or dose colorwash,
  • a suite of contouring tools to edit and/or create anatomical structures,
  • dose–volume and dose–surface histogram calculation and display tools, and
  • various predefined commands.

CERR allows the user to retrieve any AAPM/RTOG key word information about the treatment plan archive. The code is relatively self‐describing, because it relies on MATLAB structure field name definitions based on the AAPM/RTOG standard. New structure field names can be added dynamically or permanently. New components of arbitrary data type can be stored and accessed without disturbing system operation. CERR has been applied to aid research in dose–volume‐outcome modeling, Monte Carlo dose calculation, and treatment planning optimization.

In addition, radiomics tools 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.

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.

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.

In summary, CERR provides a powerful, convenient, and common framework which allows researchers to use common patient data sets, and compare and share research results.

CERR_logo

Official Website: GitHub, GitHub.io

Download: GitHub, Source Code, Compiled Version

Documentation: Wiki, Google Group, YouTube

Sampled Data: Head & Neck, ProstateLung, Orart

References: Main article, Extension for radiomics

 

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