The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation Model.
Knowledge contained within in vivo imaging annotated by human experts or computer programs is typically stored as unstructured text and separated from other associated information. The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation information model is an evo...
| Publicado en: | Journal of Digital Imaging Vol. 27; no. 6; pp. 692 - 702 |
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| Autores principales: | , , , |
| Formato: | diagnostic images tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2014
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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=103912428&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103912428 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2014 vid: 27 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103912428 99255678 10.1007/s10278-014-9710-3 NLM24934452 103912428 ppf: 692 ppct: 10 formats: fmt: @attributes: type: P tig: atl: The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation Model. aug: au: Mongkolwat, Pattanasak Kleper, Vladimir Talbot, Skip Rubin, Daniel affil: Department of Radiology, Northwestern University, 737 N. Michigan Ave, Suite 1600 Chicago 60611 USA sug: subj: Diagnostic Imaging Medical Informatics Digital Imaging Vocabulary, Controlled Documentation Standards National Cancer Institute (U.S.) Models, Theoretical XML Markup Languages DICOM Information Retrieval ab: Knowledge contained within in vivo imaging annotated by human experts or computer programs is typically stored as unstructured text and separated from other associated information. The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation information model is an evolution of the National Institute of Health's (NIH) National Cancer Institute's (NCI) Cancer Bioinformatics Grid (caBIG®) AIM model. The model applies to various image types created by various techniques and disciplines. It has evolved in response to the feedback and changing demands from the imaging community at NCI. The foundation model serves as a base for other imaging disciplines that want to extend the type of information the model collects. The model captures physical entities and their characteristics, imaging observation entities and their characteristics, markups (two- and three-dimensional), AIM statements, calculations, image source, inferences, annotation role, task context or workflow, audit trail, AIM creator details, equipment used to create AIM instances, subject demographics, and adjudication observations. An AIM instance can be stored as a Digital Imaging and Communications in Medicine (DICOM) structured reporting (SR) object or Extensible Markup Language (XML) document for further processing and analysis. An AIM instance consists of one or more annotations and associated markups of a single finding along with other ancillary information in the AIM model. An annotation describes information about the meaning of pixel data in an image. A markup is a graphical drawing placed on the image that depicts a region of interest. This paper describes fundamental AIM concepts and how to use and extend AIM for various imaging disciplines. pubtype: Academic Journal doctype: diagnostic images tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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