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This repository contains the script used to convert the TCIA LIDC-IDRI XML representation of nodule annotations and characterizations into the DICOM Segmentation object (for annotations) and DICOM Structured Reporting objects (for nodule characterizations).
The results of conversion have been released in the Standardized representation of the TCIA LIDC-IDRI annotations using DICOM analysis results collections on The Cancer Imaging Archive (TCIA).
The code in this repository served its purpose, and as such, is being archived. It is shared with the community to accompany the publication below in the spirit of transparency.
You can learn about the details of conversion in this paper, which you are encouraged to cite if you use this code or the converted dataset in your work.
Fedorov, A., Hancock, M., Clunie, D., Brochhausen, M., Bona, J., Kirby, J., Freymann, J., Pieper, S., J W L Aerts, H., Kikinis, R. & Prior, F. DICOM re-encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules. Med. Phys. (2020). doi:10.1002/mp.14445 http://dx.doi.org/10.1002/mp.14445
A second script (lidc2dicom_highdicom.py) for the same task using pure-Python
with the highdicom
package was later added by Chris Bridge.
This project has been funded in part with federal funds from the National Cancer Institute, National Institutes of Health under Contract No. HHSN261200800001E, and by the NIH grants U24 CA180918 (QIICR), U24 CA199460 and U01 CA190234. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. Under this contract the University of Arkansas for Medical Sciences is funded by Leidos Biomedical Research subcontract 16X011.