Automated Anatomic Labeling Architecture for Content Discovery in Medical Imaging Repositories.
The combination of textual data with visual features is known to enhance medical image search capabilities. However, the most advanced imaging archives today only index the studies’ available meta-data, often containing limited amounts of clinically useful information. This work proposes an anatomic...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 8; pp. 1 - 2 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2018
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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=131094281&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131094281 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2018 vid: 42 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131094281 131094281 131094281 10.1007/s10916-018-1004-8 131094281 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Anatomic Labeling Architecture for Content Discovery in Medical Imaging Repositories. aug: au: Pinho, Eduardo Costa, Carlos affil: Instituto de Engenharia Electrónica e Informática de Aveiro, DETI / IEETA - University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal sug: subj: Clinical Data Repository Models, Anatomic Automation Quality Improvement Immunohistochemistry Content Analysis Diagnostic Imaging Classification Software Databases Funding Source ab: The combination of textual data with visual features is known to enhance medical image search capabilities. However, the most advanced imaging archives today only index the studies’ available meta-data, often containing limited amounts of clinically useful information. This work proposes an anatomic labeling architecture, integrated with an open source archive software, for improved multimodal content discovery in real-world medical imaging repositories. The proposed solution includes a technical specification for classifiers in an extensible medical imaging archive, a classification database for querying over the extracted information, and a set of proof-of-concept convolutional neural network classifiers for identifying the presence of organs in computed tomography scans. The system automatically extracts the anatomic region features, which are saved in the proposed database for later consumption by multimodal querying mechanisms. The classifiers were evaluated with cross-validation, yielding a best F1-score of 96% and an average accuracy of 97%. We expect these capabilities to become common-place in production environments in the future, as automated detection solutions improve in terms of accuracy, computational performance, and interoperability. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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