Apte Aditya P, Iyer Aditi, Crispin-Ortuzar Mireia, Pandya Rutu, van Dijk Lisanne V, Spezi Emiliano, Thor Maria, Um Hyemin, Veeraraghavan Harini, Oh Jung Hun, Shukla-Dave Amita, Deasy Joseph O
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK.
Med Phys. 2018 Jun 13. doi: 10.1002/mp.13046.
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.
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.
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.
放射组学是一个不断发展的图像定量领域，但缺乏稳定且高质量的软件系统。我们扩展了放射学研究计算环境（CERR）的功能，以创建一个全面的、基于MATLAB的开源软件平台，重点在于放射组学研究的可重复性、速度和临床整合。
CERR中的放射组学工具专门设计用于结合CERR的放射学数据导入、转换、管理、图像分割和可视化等核心功能对医学图像进行定量分析。CERR允许对放射组学特征进行批量计算和可视化，并为放射组学元数据提供用户友好的数据结构。所有放射组学计算都进行了向量化以提高速度。此外，还提供了一个测试套件，用于与使用其他软件平台（如Insight Toolkit（ITK）和PyRadiomics）计算的放射组学特征进行重建和比较。CERR根据图像生物标志物标准化倡议定义的标准进行评估。CERR的放射组学特征计算通过其MATLAB应用程序编程接口与临床使用的MIM软件集成。
CERR为放射组学分析提供了一个全面的计算平台。对于计算密集型的哈拉里克纹理的矩阵公式，其速度优于ITK 4.12中的实现。对于离散化为32个区间的图像，CERR比ITK快3.5倍。CERR测试套件能够成功识别编程错误以及所测试软件包之间放射组学定义和计算的真正差异。
CERR的放射组学功能全面、开源且快速，使其成为跨机构开发和探索放射组学特征的有吸引力的平台。能够从多种放射组学实现中进行选择并与临床工作流程集成，使得CERR对回顾性和前瞻性研究分析都很有用。
