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首页> 美国卫生研究院文献>Journal of Digital Imaging >Towards Portable Large-Scale Image Processing with High-Performance Computing
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Towards Portable Large-Scale Image Processing with High-Performance Computing

机译：借助高性能计算实现便携式大规模图像处理

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High-throughput, large-scale medical image computing demands tight integration of high-performance computing (HPC) infrastructure for data storage, job distribution, and image processing. The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has constructed a large-scale image storage and processing infrastructure that is composed of (1) a large-scale image database using the eXtensible Neuroimaging Archive Toolkit (XNAT), (2) a content-aware job scheduling platform using the Distributed Automation for XNAT pipeline automation tool (DAX), and (3) a wide variety of encapsulated image processing pipelines called “spiders.” The VUIIS CCI medical image data storage and processing infrastructure have housed and processed nearly half-million medical image volumes with Vanderbilt Advanced Computing Center for Research and Education (ACCRE), which is the HPC facility at the Vanderbilt University. The initial deployment was natively deployed (i.e., direct installations on a bare-metal server) within the ACCRE hardware and software environments, which lead to issues of portability and sustainability. First, it could be laborious to deploy the entire VUIIS CCI medical image data storage and processing infrastructure to another HPC center with varying hardware infrastructure, library availability, and software permission policies. Second, the spiders were not developed in an isolated manner, which has led to software dependency issues during system upgrades or remote software installation. To address such issues, herein, we describe recent innovations using containerization techniques with XNAT/DAX which are used to isolate the VUIIS CCI medical image data storage and processing infrastructure from the underlying hardware and software environments. The newly presented XNAT/DAX solution has the following new features: (1) multi-level portability from system level to the application level, (2) flexible and dynamic software development and expansion, and (3) scalable spider deployment compatible with HPC clusters and local workstations.
机译：高通量，大规模医学图像计算要求高性能存储（HPC）基础结构的紧密集成，以用于数据存储，作业分配和图像处理。范德比尔特大学影像科学研究所（VUIIS）计算影像中心（CCI）已构建了大型图像存储和处理基础结构，该基础结构由（1）使用可扩展神经影像存档工具包（XNAT）构成的大型图像数据库组成。 ；（2）使用分布式自动化XNAT管道自动化工具（DAX）的内容感知作业调度平台，以及（3）各种各样的称为“蜘蛛”的封装图像处理管道。 VUIIS CCI医学图像数据存储和处理基础架构已通过范德比尔特研究与教育高级计算中心（ACCRE）容纳并处理了近50万张医学图像，该中心是范德比尔特大学的HPC设施。最初的部署是在ACCRE硬件和软件环境中本地部署（即直接安装在裸机服务器上），这会导致可移植性和可持续性问题。首先，将整个VUIIS CCI医学图像数据存储和处理基础架构部署到具有不同硬件基础架构，库可用性和软件许可策略的另一个HPC中心可能会很费力。其次，蜘蛛不是以孤立的方式开发的，这导致在系统升级或远程软件安装期间出现软件依赖性问题。为了解决这些问题，在这里，我们描述了使用带有XNAT / DAX的容器化技术的最新创新，该技术用于将VUIIS CCI医学图像数据存储和处理基础结构与底层硬件和软件环境隔离开来。新推出的XNAT / DAX解决方案具有以下新功能：（1）从系统级别到应用程序级别的多级可移植性；（2）灵活和动态的软件开发和扩展；以及（3）与HPC群集兼容的可扩展蜘蛛部署和本地工作站。

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