Standardized representation of the LIDC annotations using DICOM

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1 Brigham and Women's Hospital / Harvard Medical School, Boston, MA, United States
2 Enthought Inc, Austin, TX, USA
3 PixelMed Publishing, LLC, Bangor, PA, USA
4 University of Arkansas for Medical Sciences, Little Rock, AR, United States
5 Frederick National Laboratory for Cancer Research, Frederick, MD, USA
6 Isomics Inc, Cambridge, MA, USA
7 Dana Farber Cancer Institute / Harvard Medical School, Boston, MA, USA
8 Brigham and Women's Hospital / Harvard Medical School, Boston, MA, USA
9 Fraunhofer MEVIS, Bremen, Germany
10 Mathematics/Computer Science Faculty, University of Bremen, Bremen, Germany
DOI
10.7287/peerj.preprints.27378v1
Published
Accepted
Subject Areas
Bioinformatics, Oncology, Radiology and Medical Imaging
Keywords
data descriptor, cancer imaging, imaging informatics, DICOM, medical image computing, data sharing
Copyright
© 2018 Fedorov et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Fedorov A, Hancock M, Clunie D, Brochhausen M, Bona J, Kirby J, Freymann J, Pieper S, Aerts H, Kikinis R, Prior F. 2018. Standardized representation of the LIDC annotations using DICOM. PeerJ Preprints 6:e27378v1

Abstract

The Lung Imaging Data Consortium and Image Database Resource Initiative (LIDC) conducted a multi-site reader study that produced a comprehensive database of Computed Tomography (CT) scans for over 1000 subjects annotated by multiple expert readers. The result is hosted in the LIDC-IDRI collection of The Cancer Imaging Archive (TCIA). Annotations that accompany the images of the collection are stored using project-specific XML representation. This complicates their reuse, since no general-purpose tools are available to visualize or query those objects, and makes harmonization with other similar type of data non-trivial. To make the LIDC dataset more FAIR (Findable, Accessible, Interoperable, Reusable) to the research community, we prepared their standardized representation using the Digital Imaging and Communications in Medicine (DICOM) standard. This manuscript is intended to serve as a companion to the dataset to facilitate its reuse.

Author Comment

This is a preprint submission to PeerJ Preprints.