Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup.

Annotation and Image Markup (AIM) Data mining Deep learning Lesion tracking Supervised training

Journal

Journal of digital imaging
ISSN: 1618-727X
Titre abrégé: J Digit Imaging
Pays: United States
ID NLM: 9100529

Informations de publication

Date de publication:
02 2020
Historique:
pubmed: 26 2 2019
medline: 24 7 2021
entrez: 27 2 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 30805778
doi: 10.1007/s10278-019-00191-6
pii: 10.1007/s10278-019-00191-6
pmc: PMC7064644
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

49-53

Subventions

Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Organisme : NCI NIH HHS
ID : U01CA142555
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA190214
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA187947
Pays : United States

Références

Eur J Cancer. 2009 Jan;45(2):228-47
pubmed: 19097774
J Digit Imaging. 2013 Dec;26(6):1045-57
pubmed: 23884657
Radiology. 2009 Dec;253(3):590-2
pubmed: 19952021
J Digit Imaging. 2015 Feb;28(1):53-61
pubmed: 25037586
Transl Oncol. 2014 Feb 01;7(1):23-35
pubmed: 24772204
Radiographics. 2012 Sep-Oct;32(5):1543-52
pubmed: 22745220
J Digit Imaging. 2010 Apr;23(2):217-25
pubmed: 19294468

Auteurs

Nathaniel C Swinburne (NC)

Neuroradiology Section, Department of Radiology, Memorial Sloan Kettering Cancer Center, C278 Box 29, 1275 York Ave, New York, NY, 10065, USA. swinburn@mskcc.org.

David Mendelson (D)

Department of Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA.

Daniel L Rubin (DL)

Department of Biomedical Data Science, Medical School Office Building, Stanford University, Room X-335, 1265 Welch Road, Stanford, CA, 94305, USA. dlrubin@stanford.edu.

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Classifications MeSH