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
| Location | Subjects | Size | Updated | |||
|---|---|---|---|---|---|---|
| Lung | Chest | 31 | 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 2018/12/18
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 | 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 | License | |||
|---|---|---|---|---|---|---|---|---|
| Corresponding Original CT Images from Lung Phantom , LIDC-IDRI , QIN LUNG CT , and RIDER Lung CT - | CT | DICOM | Requires NBIA Data Retriever |
31 | 31 | 31 | 6,814 | CC BY 3.0 |
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 |
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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 |
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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 |
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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:
- Nodule Location Documentation (spreadsheets)
| Title | Data Type | Format | Access Points | Subjects | License |
|---|
Version 1: Updated 2015/09/15
Original release of dataset.
| Title | Data Type | Format | Access Points | Subjects | License |
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