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QIN-LUNGCT-SEG

The Cancer Imaging Archive

QIN-LungCT-Seg | QIN multi-site collection of Lung CT data with Nodule Segmentations

DOI: 10.7937/k9/tcia.2015.1buvfjr7 | Data Citation Required | Analysis Result

Cancer Types Location Subjects Related Collections Size Supporting Data Updated
Lung Chest 31 9.25GB Tumor segmentations 2018/12/18

Summary

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.

Note: In December 2018 it was discovered that an update to NSCLC Radiogenomics mistakenly resulted in the deletion of the segmentation data from this analysis set.  As a result, the 10 affected patients and related segmentations are no longer included in the download section below.  

Data Access

Version 3: Updated

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.

Title Data Type Format Access Points Subjects Studies Series Images License
Segmentations SEG DICOM
Download requires NBIA Data Retriever
31 31 378 378 CC BY 3.0
CT Images & Segmentations Combined SEG, CT DICOM
Download requires NBIA Data Retriever
31 31 409 7,192 CC BY 3.0
Lung Phantom Nodule Locations Documentation XLS CC BY 3.0
QIN LUNG CT Nodule Locations Documentation XLS CC BY 3.0
RIDER Lung CT Nodule Locations Documentation XLS CC BY 3.0
LIDC-IDRI Nodule Locations Documentation XLS CC BY 3.0

Collections Used In This Analysis Result

Title Data Type Format Access Points Subjects Studies Series Images License
Corresponding Original CT Images from Lung Phantom , LIDC-IDRI , QIN LUNG CT , and RIDER Lung CT - CT DICOM 31 31 31 6,814 CC BY 3.0

Collections Used In This Analysis Result

Related Collections

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

Kalpathy-Cramer, J., Napel, S., Goldgof, D., & Zhao, B. (2015). Multi-site collection of Lung CT data with Nodule Segmentations (version 3) [Data set]. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/k9/tcia.2015.1buvfjr7 

Detailed Description

To download all DICOM source CT Images & Segmentations Combined – 409 series  (DICOM) you can use this link : QIN Multi-site Lung CTs and SEG (minus Stanford).tcia (Download requires NBIA Data Retriever

Publications Using This Data

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

Publication Citation

Kalpathy-Cramer, J., Zhao, B., Goldgof, D., Gu, Y., Wang, X., Yang, H., Tan, Y., Gillies, R., & Napel, S. (2016). A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study. In Journal of Digital Imaging (Vol. 29, Issue 4, pp. 476–487).  https://doi.org/10.1007/s10278-016-9859-z

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

Previous Versions

Version 2: Updated 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:

1.2.276.0.7230010.3.1.3.0.34323.1424694723.968333
1.2.276.0.7230010.3.1.3.0.34343.1424694769.748096
1.2.276.0.7230010.3.1.3.0.32279.1424660367.640148
1.2.276.0.7230010.3.1.3.0.3373.1415292738.832393
1.2.276.0.7230010.3.1.3.0.32259.1424660332.352116
1.2.276.0.7230010.3.1.3.0.32238.1424660298.604243
1.2.276.0.7230010.3.1.3.0.3306.1415292638.342990
1.2.276.0.7230010.3.1.3.0.3345.1415292685.22320
1.2.276.0.7230010.3.1.3.0.34303.1424694693.127541

These have been replaced with the following new segmentation series:

1.2.276.0.7230010.3.1.3.0.21757.1437749726.319319 
1.2.276.0.7230010.3.1.3.0.21734.1437749686.271681 
1.2.276.0.7230010.3.1.3.0.21713.1437749624.694944 
1.2.276.0.7230010.3.1.3.0.95052.1441388220.839236 
1.2.276.0.7230010.3.1.3.0.95027.1441388189.267094 
1.2.276.0.7230010.3.1.3.0.95003.1441388142.544126 
1.2.276.0.7230010.3.1.3.0.3233.1437599346.502866 

Previous version spreadsheets:

Title Data Type Format Access Points Subjects Studies Series Images License

Version 1: Updated 2015/09/15

Original release of dataset.

Title Data Type Format Access Points Subjects Studies Series Images License