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Localtab
activetrue
titleData Access

Data Access


Data TypeDownload License

Thoracic Segmentations (NIfTI, zip ZIP of .nii,  402 subjects, 402 files, 26.9 MB)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/68551327/PleThora%20Thoracic_Cavities%20June%202020.zip?version=1&modificationDate=1593202695428&api=v2

Download NIfTI thoracic segmentations


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Pleural Effusion Segmentations (NIfTI, zip, 1.7 MB) 


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/68551327/PleThora%20Effusions%20June%202020.zip?version=1&modificationDate=1593202778373&api=v2

Download Effusions


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Segmentation Features and Image Metadata (CSV, 47 kb)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/68551327/Thoracic%20and%20Pleural%20Effusion%20Segmentations%20April%202020.csv?version=1&modificationDate=1585925109811&api=v2

Download thoracic and pleural effusion segmentations


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Baseline UNet 2D Summary (PDF, 1.19 MB)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/68551327/Baseline_UNet2D_summary%20July%202020.pdf?version=1&modificationDate=1595947249733&api=v2

Download Baseline UNet2D


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Baseline UNet 3D Summary (PDF, 746 kb)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/68551327/Baseline_UNet3D_summary%20July%202020.pdf?version=1&modificationDate=1595947224280&api=v2

Download Baseline UNet3D


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Data Dictionary (DOCX, 19 kb)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/68551327/PleThora%20data_dictionary%20July%202020.docx?version=1&modificationDate=1596226993554&api=v2

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 TypeDownload License
Corresponding Original CT Images from 22517100 NSCLC-Radiomics (DICOM, 402 subjects, 24 GB)
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urlhttps://wiki.cancerimagingarchive.net/download/attachments/68551327/NSCLC-Radiomics-OriginalCTs.tcia?version=1&modificationDate=1586193102017&api=v2

Download NSCLC-Radiomics-OriginalCTs.tcia

(Download requires NBIA Data Retriever)

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Localtab
titleDetailed Description

Detailed Description

All 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.


Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy


Tcia limited license policy


Info
titleData Citation

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 .


Info
titleAcknowledgement - Grant support

Swiss Cancer League (BIL KLS-4300-08-2017).


Info
titleAcknowledgement - Grant support

Learning Healthcare Award funded by the UTHealth Center for Clinical and Translational Science (CCTS).


Info
titleAcknowledgement - Grant support

NIH grant UL1TR003167.


Info
titleAcknowledgement - Grant support

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)


Info
titleAcknowledgement - Grant support

National Cancer Institute (NCI) Early Phase Clinical Trials in Imaging and Image-Guided Interventions Program  (1R01CA218148)


Info
titleAcknowledgement - Grant support

NIH/NCI Cancer Center Support Grant (CCSG) Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672)


Info
titleAcknowledgement - Grant support

NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award (P50 CA097007)


Info
titleAcknowledgement - Grant support

National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679)


Info
titleAcknowledgement - Grant support

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)


Info
titleAcknowledgement - Grant support

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).


Info
titleAcknowledgement - Grant support

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.


Info
titleAcknowledgement - Grant support

Direct industry grant support, honoraria, and travel funding from Elekta AB


Info
titlePublication Citation

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
(2020 medRxiv preprint doi): https://doi.org/10.1101/2020.05.14.20102103.


Info
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. 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:


Info
titleData Citation

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 Data

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




Localtab
titleVersions

Version 3 (Current): Updated 2020/07/28

Data TypeDownload all or Query/Filter
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 TypeDownload all or Query/Filter
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 TypeDownload all or Query/Filter

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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