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title | Data Access |
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| Data AccessClick the Download button to save the data.
Data Type | Download | License |
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Thoracic Segmentations (NIfTIZIP of .nii, 402 subjects, 402 files, 26.9 MB zip) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/68551327/PleThora%20Thoracic_Cavities%20June%202020.zip?version=1&modificationDate=1593202695428&api=v2 |
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| Download NIfTI thoracic segmentations |
| | Pleural Effusion Segmentations (NIfTI, zip, 1.7 MB zip) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/68551327/PleThora%20Effusions%20June%202020.zip?version=1&modificationDate=1593202778373&api=v2 |
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| Download Effusions |
| | Segmentation Features and Image Metadata (CSV, 47 kb) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/68551327/Thoracic%20and%20Pleural%20Effusion%20Segmentations%20April%202020.csv?version=1&modificationDate=1585925109811&api=v2 |
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| Download thoracic and pleural effusion segmentations |
| | Baseline UNet 2D Summary (PDF, 1.19 MB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/68551327/Baseline_UNet2D_summary%20July%202020.pdf?version=1&modificationDate=1595947249733&api=v2 |
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| Download Baseline UNet2D |
| | Baseline UNet 3D Summary (PDF, 746 kb) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/68551327/Baseline_UNet3D_summary%20July%202020.pdf?version=1&modificationDate=1595947224280&api=v2 |
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| Download Baseline UNet3D |
| | Data Dictionary (DOCX, 19 kb) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/68551327/PleThora%20data_dictionary%20July%202020.docx?version=1&modificationDate=1596226993554&api=v2 |
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| Download PleThora Data Dictionary |
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Click the Versions tab for more info about data releases. Collections Used in this Third Party Analyses Below is a list of the Collections used in these analyses:
Source Data Type | Download | License |
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Corresponding Original CT Images from 22517100 NSCLC-Radiomics (DICOM, 402 subjects, 24 GB) | Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/68551327/NSCLC-Radiomics-OriginalCTs.tcia?version=1&modificationDate=1586193102017&api=v2 |
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| Download NSCLC-Radiomics-OriginalCTs.tcia |
(Download requires NBIA Data Retriever) | |
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title | Detailed Description |
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| Detailed DescriptionAll NIfTI files have been compressed for convenience (.nii.gz) Note: These segmentations use the RPI orientation, but the original DICOM files are oriented using the RAI convention. As a result, some tools such as ITK-SNAP will not render the segmentations in the correct orientation when visualized. The authors of these data suggest using software like Mango (http://ric.uthscsa.edu/mango/) or to convert to DICOM files to NIfTI with software like dcm2niix (https://github.com/rordenlab/dcm2niix) to address this issue. |
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy
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| Kiser, K.J., Ahmed, S., Stieb, S.M., Mohamed, A.S.R., Elhalawani, H., Park, P.Y.S., Doyle, N.S., Wang, B.J., Barman, A., Fuller, C.D., Giancardo, L. (2020). Data from the Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines (PleThora) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2020.6c7y-gq39 . |
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title | Acknowledgement - Grant support |
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| Swiss Cancer League (BIL KLS-4300-08-2017). |
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title | Acknowledgement - Grant support |
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| Learning Healthcare Award funded by the UTHealth Center for Clinical and Translational Science (CCTS). |
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title | Acknowledgement - Grant support |
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| NIH grant UL1TR003167. |
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title | Acknowledgement - Grant support |
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| National Institutes of Health (NIH) National Institute for Dental and Craniofacial Research Establishing Outcome Measures Award (1R01DE025248/R56DE025248) and an Academic Industrial Partnership Grant (R01DE028290) |
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title | Acknowledgement - Grant support |
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| National Cancer Institute (NCI) Early Phase Clinical Trials in Imaging and Image-Guided Interventions Program (1R01CA218148) |
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title | Acknowledgement - Grant support |
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| NIH/NCI Cancer Center Support Grant (CCSG) Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672) |
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title | Acknowledgement - Grant support |
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| NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award (P50 CA097007) |
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title | Acknowledgement - Grant support |
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| National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679) |
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title | Acknowledgement - Grant support |
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| NSF Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) standard grant (NSF 1933369) a National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Education Programs for Residents and Clinical Fellows Grant (R25EB025787-01) |
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title | Acknowledgement - Grant support |
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| NIH Big Data to Knowledge (BD2K) Program of the NCI Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award (1R01CA214825). |
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title | Acknowledgement - Grant support |
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| Direct infrastructure support was provided by the multidisciplinary Stiefel Oropharyngeal Research Fund of the University of Texas MD Anderson Cancer Center Charles and Daneen Stiefel Center for Head and Neck Cancer and the Cancer Center Support Grant (P30CA016672) and the MD Anderson Program in Image-guided Cancer Therapy. |
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title | Acknowledgement - Grant support |
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| Direct industry grant support, honoraria, and travel funding from Elekta AB |
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title | Publication Citation |
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| Kiser, K.J., Barman, A., Stieb, S., Fuller, C.D., Giancardo, L., 2021. Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow. J Digit Imaging. https://doi.org/10.1007/s10278-021-00460-3 PMID: 34027588 PMCID: PMC8329111 PMC8329111 (2020 medRxiv preprint doi): https://doi.org/10.1101/2020.05.14.20102103. |
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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 PMCID: PMC3824915 |
In addition to the dataset citation above, please be sure to also cite the following if you utilize these data in your research:
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| Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., & Lambin, P. (2019). Data From NSCLC-Radiomics (Version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI |
Other Publications Using This DataTCIA maintains a list of publications which leverage our data. If you have a manuscript you'd like to add please contact TCIA's Helpdesk. |
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| Version 3 (Current): Updated 2020/07/28 Data Type | Download all or Query/Filter |
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Corresponding Original CT Images (DICOM) from NSCLC-Radiomics (24 GB) | | Thoracic Segmentations (NIfTI, 26.9 MB) | | Pleural Effusion Segmentations (NIfTI, 1.7 MB) | | Segmentation Features and Image Metadata (CSV) | | Baseline UNet 2D Summary (PDF) | | Baseline UNet 3D Summary (PDF) | | Data Dictionary (DOCX) | | Version 3 changes: 2D U-Net - Incorrectly reported the 2D U-Net achieved segmentations with Dice similarity coefficients of 0.90 and 0.94 for left and right lungs.
- The performances should be 0.94 and 0.94.
3D U-Net - Incorrectly reported the 3D U-Net achieved segmentations with Dice similarity coefficients of 0.82 and 0.94 for left and right lungs.
- The performances should be 0.95 and 0.96.
Data Dictionary Added Auto-MS Thorax DSC description. Version 2: 2020/06/26 Data Type | Download all or Query/Filter |
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Corresponding Original CT Images (DICOM) from NSCLC-Radiomics (24 GB) | | Thoracic Segmentations (NIfTI, 26.9 MB) | | Pleural Effusion Segmentations (NIfTI, 1.7 MB) | | Segmentation Features and Image Metadata (CSV) | | Baseline UNet 2D Summary (PDF) | | Baseline UNet 3D Summary (PDF) | | Data Dictionary (DOCX) | | Version 2 changes: - The dataset is now named “PleThora” for “Pleural effusion and thoracic cavity segmentations in diseased lungs.”
- All NIfTI files have been compressed for convenience (.nii à .nii.gz)
- All thoracic cavity primary reviewer segmentations have been renamed from “lungMask_edit.nii” to “[CaseID]_thor_cav_primary_reviewer.nii.gz” to more specifically identify each file’s contents and avoid confusion.
- Eighty-six thoracic cavity secondary reviewer segmentations have been added. These are named “[CaseID]_thor_cav_secondary_reviewer.nii.gz.”
- Interobserver variability analysis between primary and secondary reviewer thoracic cavity segmentations revealed four cases in which interobserver agreement was anomalously lower than all other cases. These cases were manually re-reviewed by another physician. In three cases (LUNG1-026, LUNG1-157, and LUNG1-354) it was deemed that the secondary reviewer’s segmentation excluded structures that should have been included. These were corrected. In one case (LUNG-088) it was determined that the primary reviewer segmentation included a large (400 cm3) nodal conglomerate. Our original thoracic cavity segmentation definition did not intend to include nodal conglomerates, so for consistency’s sake we corrected the primary reviewer segmentation accordingly. However, the segmentation with the nodal conglomerate is still valuable, so we provide it as well and name it “LUNG1-088_thor_cav_primary_reviewer_with_nodal_conglomerate.nii”
- We manually reviewed the pleural effusion segmentations of the primary physician reviewer and determined that in many cases the reviewer had not been sufficiently careful. Therefore, all 78 primary reviewer segmentations were re-reviewed by another physician and corrected as necessary. They are now re-submitted as “[CaseID]_effusion_first_reviewer.nii.gz”
- Seventy-eight pleural effusion secondary reviewer segmentations have been added. These are named “[CaseID]_effusion_second_reviewer.nii.gz.”
- Fifteen pleural effusion tertiary reviewer segmentations have been added. These are named “[CaseID]_effusion_third_reviewer.nii.gz.”
- We add two documents that describe baseline performances for 2D and 3D U-Net segmentation algorithms and define a reproducible train/test split.
- Data Dictionary: we provide a data dictionary to describe the meanings of column names in the “Thorax and Pleural Effusion Segmentation Metadata” spreadsheet.
Version 1: 2020/04/03
Data Type | Download all or Query/Filter |
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Thoracic Segmentations (NIfTI, 54.7 MB zipped, 23.6 GB uncompressed) | | Pleural Effusion Segmentations (NIfTI, 5.3 MB zipped, 4.9 GB uncompressed) | | Segmentation Features and Image Metadata (CSV) | | Corresponding Original CT Images (DICOM) from NSCLC-Radiomics (24 GB) | |
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