Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA.
Department of Electrical Engineering, Stanford University, 650 Serra Mall, Stanford, CA, 94305, USA.
J Digit Imaging. 2018 Aug;31(4):403-414. doi: 10.1007/s10278-017-0019-x.
The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.
本研究旨在开发一种开源、模块化、本地运行或基于服务器的 3D 放射组学特征计算系统，可在任何计算机系统上使用，并可集成到现有的工作流程中，以了解图像特征与临床数据（如生存）之间的关联并构建预测模型。QIFE 利用各种级别的并行化在多核系统上运行。它由一个管理框架和四个阶段组成：输入、预处理、特征计算和输出。每个阶段包含一个或多个可交换组件，允许在运行时进行自定义。我们使用不同级别的并行化在包含 108 个肺部肿瘤的 CT 扫描队列上对引擎进行了基准测试。已经发布了两个版本的 QIFE：（1）发布到 Github 的开源 MATLAB 代码，（2）一个编译版本加载到 Docker 容器中，并发布到 DockerHub，可以轻松部署在任何计算机上。QIFE 使用 1 个核心在 2:12（小时/分钟）内处理了 108 个对象（肿瘤），使用 4 个核心的对象级并行化在 1:04（小时/分钟）内处理了 108 个对象。我们开发了定量图像特征引擎（QIFE），这是一个开源的特征提取框架，专注于模块化、标准、并行化、来源和集成。研究人员可以通过创建实现其现有接口的输入和输出组件，轻松地将其与现有的分割和成像工作流程集成。通过以牺牲内存使用为代价并行执行，可以提高计算效率。不同的并行化级别提供不同的权衡，最佳设置将取决于要处理的数据集的大小和组成。
