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  • QIN multi-site collection of Lung CT data with Nodule Segmentations (QIN-LungCT-Seg)

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This dataset (also known as the “moist run” among QIN sites) contains CT images (41 total scans) of non-small cell lung cancer from: the Reference Image Database to Evaluate Therapy Response (RIDER), the Lung Image Database Consortium (LIDC), patients from Stanford University Medical Center and the Moffitt Cancer Center, and the Columbia University/FDA Phantom. In addition, 3 academic institutions (Columbia, Stanford, Moffitt-USF) each ran their own segmentation algorithm on a total of 52 tumor volumes.  Segmentations were performed 3 different times with different initial conditions, resulting in 9 segmentations formatted as DICOM Segmentation Objects (DSOs) for each tumor volume, for a total of 468 segmentations. This collection may be useful for designing and comparing competing segmentation algorithms, for establishing acceptable ranges of variability in volume and segmentation borders, and for developing algorithms for creating cancer biomarkers from features computed from the segmented tumors and their environments.


Localtab Group

titleData Access

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
CT Images - 31 series (DICOM)

Segmentations - 378 series (DICOM)

CT Images & Segmentations Combined - 409 series  (DICOM)

Nodule Location Documentation (spreadsheets)

Please contact  with any questions regarding usage.

Collections Used in this Third Party Analysis
Below is a list of the Collections used in these analyses:

titleDetailed Description

Detailed Description

Previous version spreadsheets:

For more information on versioning, please refer to the Versions tab.

titleCitations & Data Usage Policy

Citations & Data Usage Policy 

Public collection license

titleData Citation

Jayashree Kalpathy-Cramer, Sandy Napel, Dmitry Goldgof, Binsheng Zhao. (2015). Multi-site collection of Lung CT data with Nodule Segmentations. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2015.1BUVFJR7

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, Volume 26, Number 6 pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

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

titlePublication Citation

Kalpathy-Cramer, J., Zhao, B., Goldgof, D., Gu, Y., Wang, X., Yang, H., … Napel, S. (2016, February 3). A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study. Journal of Digital Imaging. Springer Nature. DOI: 10.1007/s10278-016-9859-z

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): 2018/12/18

Data TypeDownload all or Query/Filter
CT Images - 31 series (DICOM)

Segmentations - 378 series (DICOM)

CT Images & Segmetations Combined - 409 series  (DICOM)

Note: In December 2018 it was discovered that an update to NSCLC Radiogenomics mistakenly resulted in the deletion of the segmentation data for this analysis set.  As a result, version 3 excludes the Stanford NSCLC Radiogenomics subset of the analyses.

Version 2: 2015/12/21

On 9/14/2015 this DOI was updated to resolve problems with 9 of the segmentations being incorrectly labeled.  The Series Instance UIDs in the original data set which have since been deleted from TCIA are:

These have been replaced with the following new segmentation series: 

Version 1: 2015/09/15

Original release of dataset.