DICOM-LIDC-IDRI-Nodules | Standardized representation of the TCIA LIDC-IDRI annotations using DICOM
DOI: 10.7937/TCIA.2018.h7umfurq | Page Accessibility: Public | Analysis Result
| Location | Subjects | Updated | |||
|---|---|---|---|---|---|
| Lung | Chest | 1,010 | Tumor segmentations, image features | 03/26/2020 |
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 Data from The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans (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
Please contact [email protected] 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.
- pylidc library (https://pylidc.github.io/)
- dcmqi library (https://github.com/qiicr/dcmqi)
- The script used for the conversion is available at https://github.com/qiicr/lidc2dicom
Collections Used in this Third Party Analysis
Below is a list of the Collections used in these analyses:
| Title | Data Type | Format | Access Points | License | |||
|---|---|---|---|---|---|---|---|
| Structured Reports and Segmentations | SR, SEG | SR and SEG | Requires NBIA Data Retriever |
883 | 13,718 | 13,718 | CC BY 3.0 |
| DSO Key | CSV | CC BY 3.0 |
Citations & Data Usage Policy
Data 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 |
Publication 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 |
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. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7 |
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.