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  • Standardized representation of the TCIA LIDC-IDRI annotations using DICOM (DICOM-LIDC-IDRI-Nodules)

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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 tabsection).


Localtab Group



Localtab
activetrue
titleData Access

Data Access



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

  

Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/44499647/LIDC-IDRI-StandardizedRepresentation-March2020-manifest.tcia?api=v2


(Download requires the NBIA Data Retriever)

Tcia cc by 3

DSO Key (csv)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/44499647/TCGA_DSO_Key.csv?api=v2



Tcia cc by 3

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

Additional Resources for this Dataset

The following external resources have been made available by the data submitters.  These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.

Collections Used in this Third Party Analysis

Below is a list of the Collections used in these analyses:




Localtab
titleDetailed Description

Detailed Description

Image

Image  Statistics


Modalities (DICOM)SEG, SR
Number of Patients875
Number of Studies

883

Number of Series
13,718
13718
Number of Images
13,718
13718
Images Size (GB)2
GB
.34





Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

Tcia limited license policy


Info
titleData Citation

Fedorov, A., Hancock, M., Clunie, D., Brockhhausen, M., Bona, J., Kirby, J., Freymann, J., Aerts, H.J.W.L., Kikinis, R., Prior, F. (2018). Standardized representation of the TCIA LIDC-IDRI annotations using DICOM. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2018.h7umfurq


Info
titlePublication Citation

Fedorov, A., Hancock, M., Clunie,  D., Brochhausen, M., Bona, J., Kirby, J., Freymann, J, Pieper S, Aerts H.J.W.L., Kikinis, R., Prior, F. (2020) DICOM re‐encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules. Medical Physics Dataset Article. https://doi.org/10.1002/mp.14445


Info
titleTCIA 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, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7

Additional Publication Resources:

The Collection authors suggest the below will give context to this dataset:

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

    • Armato SG III, 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: https://doi.org/10.1118/1.3528204
    • Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Van Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., Brown, M. S., Engelmann, R. M., Laderach, G. E., Max, D., Pais, R. C. , Qing, D. P. Y. , Roberts, R. Y., Smith, A. R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G. W., Jude, C. M., Munden, R. F., Petkovska, I., Quint, L. E., Schwartz, L. H., Sundaram, B., Dodd, L. E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A. V., Gupte, S., Sallam, M., Heath, M. D., Kuhn, M. H., Dharaiya, E., Burns, R., Fryd, D. S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B. Y., Clarke, L. P. (2015). Data From LIDC-IDRI [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX

Other Publications Using This Data

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





Localtab
titleVersions

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. 




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