Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup.
Sharing radiologic image annotations among multiple institutions is important in many clinical scenarios; however, interoperability is prevented because different vendors' PACS store annotations in non-standardized formats that lack semantic interoperability. Our goal was to develop software to auto...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 1; pp. 49 - 54 |
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| Autores principales: | , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2020
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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=142164497&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142164497 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2020 vid: 33 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142164497 142164497 142164497 10.1007/s10278-019-00191-6 142164497 ppf: 49 ppct: 5 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup. aug: au: Swinburne, Nathaniel C. Mendelson, David Rubin, Daniel L. affil: Neuroradiology Section, Department of Radiology, Memorial Sloan Kettering Cancer Center, C278 Box 29, 1275 York Ave, 10065, New York, NY, USA sug: subj: Diagnostic Imaging Electronic Data Interchange Semantics Automation Software Design Data Mining Metadata Deep Learning ab: Sharing radiologic image annotations among multiple institutions is important in many clinical scenarios; however, interoperability is prevented because different vendors' PACS store annotations in non-standardized formats that lack semantic interoperability. Our goal was to develop software to automate the conversion of image annotations in a commercial PACS to the Annotation and Image Markup (AIM) standardized format and demonstrate the utility of this conversion for automated matching of lesion measurements across time points for cancer lesion tracking. We created a software module in Java to parse the DICOM presentation state (DICOM-PS) objects (that contain the image annotations) for imaging studies exported from a commercial PACS (GE Centricity v3.x). Our software identifies line annotations encoded within the DICOM-PS objects and exports the annotations in the AIM format. A separate Python script processes the AIM annotation files to match line measurements (on lesions) across time points by tracking the 3D coordinates of annotated lesions. To validate the interoperability of our approach, we exported annotations from Centricity PACS into ePAD (http://epad.stanford.edu) (Rubin et al., Transl Oncol 7(1):23–35, 2014), a freely available AIM-compliant workstation, and the lesion measurement annotations were correctly linked by ePAD across sequential imaging studies. As quantitative imaging becomes more prevalent in radiology, interoperability of image annotations gains increasing importance. Our work demonstrates that image annotations in a vendor system lacking standard semantics can be automatically converted to a standardized metadata format such as AIM, enabling interoperability and potentially facilitating large-scale analysis of image annotations and the generation of high-quality labels for deep learning initiatives. This effort could be extended for use with other vendors' PACS. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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