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Published in: Journal of Digital Imaging 1/2020

01-02-2020

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

Authors: Nathaniel C. Swinburne, David Mendelson, Daniel L. Rubin

Published in: Journal of Imaging Informatics in Medicine | Issue 1/2020

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Abstract

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.
Literature
1.
go back to reference Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F: The CANCER Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. J Digit Imaging 26(6):1045–1057, 2013CrossRef Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F: The CANCER Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. J Digit Imaging 26(6):1045–1057, 2013CrossRef
2.
go back to reference Langer SG, Tellis W, Carr C, Daly M, Erickson BJ, Mendelson D, Moore S, Perry J, Shastri K, Warnock M, Zhu W: The RSNA Image Sharing Network. J Digit Imaging 28(1):53–61, 2015CrossRef Langer SG, Tellis W, Carr C, Daly M, Erickson BJ, Mendelson D, Moore S, Perry J, Shastri K, Warnock M, Zhu W: The RSNA Image Sharing Network. J Digit Imaging 28(1):53–61, 2015CrossRef
3.
go back to reference Channin DS, Mongkolwat P, Kleper V, Rubin DL: The Annotation and Image Mark-up Project. Radiology 253(3):590–592, 2009CrossRef Channin DS, Mongkolwat P, Kleper V, Rubin DL: The Annotation and Image Mark-up Project. Radiology 253(3):590–592, 2009CrossRef
4.
go back to reference Channin DS, Mongkolwat P, Kleper V, Sepukar K, Rubin DL: The caBIG™ Annotation and Image Markup Project. J Digit Imaging 23(2):217–225, 2010CrossRef Channin DS, Mongkolwat P, Kleper V, Sepukar K, Rubin DL: The caBIG™ Annotation and Image Markup Project. J Digit Imaging 23(2):217–225, 2010CrossRef
7.
go back to reference Rubin DL, Willrett D, O’Connor MJ, Hage C, Kurtz C, Moreira DA: Automated Tracking of Quantitative Assessments of Tumor Burden in Clinical Trials. Transl Oncol 7(1):23–35, 2014CrossRef Rubin DL, Willrett D, O’Connor MJ, Hage C, Kurtz C, Moreira DA: Automated Tracking of Quantitative Assessments of Tumor Burden in Clinical Trials. Transl Oncol 7(1):23–35, 2014CrossRef
8.
go back to reference Abajian AC, Levy M, Rubin DL: Informatics in radiology: improving clinical work flow through an AIM database: a sample web-based lesion tracking application. Radiogr Rev Publ Radiol Soc N Am Inc 32(5):1543–1552, 2012 Abajian AC, Levy M, Rubin DL: Informatics in radiology: improving clinical work flow through an AIM database: a sample web-based lesion tracking application. Radiogr Rev Publ Radiol Soc N Am Inc 32(5):1543–1552, 2012
9.
go back to reference Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J: New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247, 2009CrossRef Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J: New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247, 2009CrossRef
10.
go back to reference Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L: ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis 115(3):211–252, 2015CrossRef Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L: ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis 115(3):211–252, 2015CrossRef
11.
go back to reference Yan K, Wang X, Lu L, Summers RM: Deep Lesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations. ArXiv171001766 Cs [Internet]. 2017 Oct 4 [cited 2018 Aug 20]; Available from: http://arxiv.org/abs/1710.01766. Accessed 16 Oct 2018 Yan K, Wang X, Lu L, Summers RM: Deep Lesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations. ArXiv171001766 Cs [Internet]. 2017 Oct 4 [cited 2018 Aug 20]; Available from: http://​arxiv.​org/​abs/​1710.​01766. Accessed 16 Oct 2018
Metadata
Title
Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup
Authors
Nathaniel C. Swinburne
David Mendelson
Daniel L. Rubin
Publication date
01-02-2020
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 1/2020
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-019-00191-6

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