Towards Portable Large-Scale Image Processing with High-Performance Computing.
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 const...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 3; pp. 304 - 315 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | diagnostic images tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129685406&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129685406 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2018 vid: 31 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129685406 129685406 129685406 10.1007/s10278-018-0080-0 129685406 ppf: 304 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Towards Portable Large-Scale Image Processing with High-Performance Computing. aug: au: Huo, Yuankai Boyd, Brian D. Parvathaneni, Prasanna Blaber, Justin Damon, Stephen M. Landman, Bennett A. Bao, Shunxing Chaganti, Shikha Nath, Vishwesh Lyu, Ilwoo Noguera, Camilo Bermudez Greer, Jasmine M. Rogers, Baxter P. French, William R. Newton, Allen T. affil: Electrical Engineering, Vanderbilt University, 2201 West End Ave, 37235, Nashville, TN, USA sug: subj: Image Processing, Computer Assisted Systems Design Data Management Information Storage Software ab: 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. pubtype: Academic Journal doctype: diagnostic images tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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