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Summary

This dataset contains standardized DICOM representation of the annotations and characterizations collected by the LIDC/IDRI initiative, originally stored in XML and available in the TCIA LIDC-IDRI collection. Only the nodules that were deemed to be greater or equal to 3 mm in the largest planar dimensions have been annotated and characterized by the expert radiologists performing the annotations. Only those nodules are included in the present dataset.

Conversion was enabled by the pylidc library (https://pylidc.github.io/) (parsing of XML, volumetric reconstruction of the nodule annotations, clustering of the annotations belonging to the same nodule, calculation of the volume, surface area and largest diameter of the nodules) and the dcmqi library (https://github.com/qiicr/dcmqi) (storing of the annotations into DICOM Segmentation objects, and storing of the characterizations and measurements into DICOM Structured Reporting objects). The script used for the conversion is available at https://github.com/qiicr/lidc2dicom. The details on the process of the conversion and the usage of the resulting objects are available in the preprint citation (see Citations & Data Usage Policy tab).


Data Access

Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever.

Data TypeDownload all or Query/Filter
Structured Reports (SR) and Segmentations (DICOM)

Please contact help@cancerimagingarchive.net  with any questions regarding usage.

Detailed Description

Image Statistics


Modalities (DICOM)

Seg, SR

Number of Patients

875

Number of Studies

883

Number of Series

13,718

Number of Images

13,718

Images Size (GB)2 GB

Citations & Data Usage Policy 

These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:

Data Citation

Andrey Fedorov, Matthew Hancock, David Clunie, Mathias Brockhausen, Jonathan Bona, Justin Kirby, John Freymann, Hugo Aerts, Ron Kikinis, Fred Prior. Standardized representation of the TCIA LIDC-IDRI annotations using DICOM. (2018) The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2018.h7umfurq

TCIA Citation

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. (2013) The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6 pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

Preprint Citation

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 https://doi.org/10.7287/peerj.preprints.27378

In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:

Publication Citation

Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, et al.:  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics, 38: 915--931, 2011. DOI: 10.1118/1.3528204

Data Citation

Armato III, Samuel G., McLennan, Geoffrey, Bidaut, Luc, McNitt-Gray, Michael F., Meyer, Charles R., Reeves, Anthony P., … Clarke, Laurence P. (2015). Data From LIDC-IDRI. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX

Other Publications Using This Data

TCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

Version 3 (Current): 2020/03/26

Data TypeDownload all or Query/Filter
Structured Reports (SR) and Segmentations (DICOM)

What changed:

DICOM objects curated and added to the cancerimagingarchive.net

Version 2: 2019/05/14

Data TypeDownload all or Query/Filter
Structured Reports (SR) and Segmentations (DICOM)

What changed: DICOM SEG objects no longer encode empty slices to reduce object size. The coded terms used to describe the nodule annotations now use fewer non-standard (99QIICR) codes. SegmentLabel attribute is populated in the DICOM SEG objects to list nodule annotation name instead of "Nodule", to help with readability
for the user.

Version 1: 2018/11/30

Data TypeDownload all or Query/Filter
Structured Reports (SR) and Segmentations (DICOM)

Note: Version 1 of this dataset is currently located in a shared Google Drive folder while undergoing verification. When testing is complete the Google Drive folder will be replaced by a different link to the final dataset. If you identify any issues with the data please report them to the TCIA Helpdesk.

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