QIN-LungCT-Seg | QIN multi-site collection of Lung CT data with Nodule Segmentations
DOI: 10.7937/k9/tcia.2015.1buvfjr7 | Page Accessibility: Public | Analysis Result
| Location | Subjects | Updated | |||
|---|---|---|---|---|---|
| Lung | Chest | 31 | Tumor segmentations | 12/18/2018 |
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
Please contact [email protected] with any questions regarding usage.
| Title | Data Type | Format | Access Points | License | |||
|---|---|---|---|---|---|---|---|
| Segmentations - | DICOM | Requires NBIA Data Retriever |
378 | CC BY 3.0 | |||
| CT Images & Segmentations Combined - | DICOM | Requires NBIA Data Retriever |
409 | 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 Third Party Analysis
| Title | Data Type | Format | Access Points | License | |||
|---|---|---|---|---|---|---|---|
| Corresponding Original CT Images from Lung Phantom , LIDC-IDRI , QIN LUNG CT , and RIDER Lung CT - | DICOM | Requires NBIA Data Retriever |
31 | CC BY 3.0 |
Detailed Description
For more information on versioning, please refer to the Versions tab.
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)
Citations & Data Usage Policy
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 |
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 |
Other 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.