Literature DB >> 30805778

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

Nathaniel C Swinburne1, David Mendelson2, Daniel L Rubin3.   

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.

Entities:  

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

Mesh:

Year:  2020        PMID: 30805778      PMCID: PMC7064644          DOI: 10.1007/s10278-019-00191-6

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  7 in total

1.  The Annotation and Image Mark-up project.

Authors:  David S Channin; Pattanasak Mongkolwat; Vladimir Kleper; Daniel L Rubin
Journal:  Radiology       Date:  2009-12       Impact factor: 11.105

2.  Informatics in radiology: improving clinical work flow through an AIM database: a sample web-based lesion tracking application.

Authors:  Aaron C Abajian; Mia Levy; Daniel L Rubin
Journal:  Radiographics       Date:  2012-06-27       Impact factor: 5.333

3.  The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.

Authors:  Kenneth Clark; Bruce Vendt; Kirk Smith; John Freymann; Justin Kirby; Paul Koppel; Stephen Moore; Stanley Phillips; David Maffitt; Michael Pringle; Lawrence Tarbox; Fred Prior
Journal:  J Digit Imaging       Date:  2013-12       Impact factor: 4.056

4.  Automated tracking of quantitative assessments of tumor burden in clinical trials.

Authors:  Daniel L Rubin; Debra Willrett; Martin J O'Connor; Cleber Hage; Camille Kurtz; Dilvan A Moreira
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

5.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).

Authors:  E A Eisenhauer; P Therasse; J Bogaerts; L H Schwartz; D Sargent; R Ford; J Dancey; S Arbuck; S Gwyther; M Mooney; L Rubinstein; L Shankar; L Dodd; R Kaplan; D Lacombe; J Verweij
Journal:  Eur J Cancer       Date:  2009-01       Impact factor: 9.162

Review 6.  The RSNA Image Sharing Network.

Authors:  S G Langer; W Tellis; C Carr; M Daly; B J Erickson; D Mendelson; S Moore; J Perry; K Shastri; M Warnock; W Zhu
Journal:  J Digit Imaging       Date:  2015-02       Impact factor: 4.056

7.  The caBIG annotation and image Markup project.

Authors:  David S Channin; Pattanasak Mongkolwat; Vladimir Kleper; Kastubh Sepukar; Daniel L Rubin
Journal:  J Digit Imaging       Date:  2009-03-18       Impact factor: 4.056

  7 in total
  1 in total

1.  Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations.

Authors:  Nathaniel C Swinburne; Vivek Yadav; Julie Kim; Ye R Choi; David C Gutman; Jonathan T Yang; Nelson Moss; Jacqueline Stone; Jamie Tisnado; Vaios Hatzoglou; Sofia S Haque; Sasan Karimi; John Lyo; Krishna Juluru; Karl Pichotta; Jianjiong Gao; Sohrab P Shah; Andrei I Holodny; Robert J Young
Journal:  Radiology       Date:  2022-01-18       Impact factor: 11.105

  1 in total

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