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
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
49-53Subventions
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
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