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  • Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines (PleThora)

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Summary


Automated or semi-automated algorithms intended for chest CT analyses typically require the creation of a 3D map of the thoracic volume as their initial step. Identifying this anatomic region precedes fundamental tasks such as lung structure segmentation, lesion detection, and radiomics feature extraction in analysis pipelines. However, automatic approaches to segment the thoracic volume maps struggle to perform consistently in subjects with diseased lungs, yet this is exactly the circumstance for which pipeline analyses would be most useful. To address this need, we have created a dataset of thoracic volume segmentations on subjects with diseased lungs. These will help the research community compare and contrast their approaches for this foundational processing step on clinically relevant data.This dataset consists of left and right thoracic volume segmentations delineated on 402 CT scans from The Cancer Imaging Archive NSCLC Radiomics collection. Thoracic segmentations include lung parenchyma, tumor, atelectasis, and effusion when present. On scans where effusion is present, separate segmentations labeling pleural effusion alone are also provided.


Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:

  • University of Texas M.D. Anderson Cancer Center, Houston, TX, USA - Special thanks to Kendall Kiser, MS Biomedical Informatics, from the Department of Radiation Oncology.
  • The University of Texas Health Science Center School of Biomedical Informatics, Houston, TX, USA

Data Access

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Detailed Description

Image Statistics


Modalities

Seg

Number of Patients

402

Number of Studies

402

Number of Series


Number of Images


Images Size (GB)

Table will be populated once all data received. Any additional data description will be added here.

This dataset consists of left and right thoracic volume segmentations delineated on 402 CT scans from The Cancer Imaging Archive NSCLC Radiomics collection. Thoracic segmentations include lung parenchyma, tumor, atelectasis, adhesions, and effusion, when present. The initial thoracic segmentations were first generated automatically by a U-Net based algorithm trained on chest CTs without cancer, manually corrected by a medical student, and revised by a radiation oncologist or a radiologist.
On scans where effusion is present, separate segmentations labeling pleural effusion alone are also provided. Pleural effusion segmentations were manually delineated by a medical student and revised by a radiologist. Expert GTV segmentations already provided by the NSCLC Radiomics collection helped inform our segmentations, and areas of the effusion that overlap with GTVs are not included. Researchers interested in discriminating between GTV and effusion using imaging biomarker inputs may find our pleural effusion segmentations useful, especially when paired with the GTV segmentations provided in the NSCLC Radiomics collection.
Tabular data extending the tabular data accompanying the NSCLC Radiomics collection are also provided, including GTV, thorax, and effusion volumes (in cm3), tumor location, and image metadata. This dataset was utilized in a clinical informatics study correlating autosegmentation accuracy as gauged by several common metrics with time required for manual corrections.

Citations & Data Usage Policy

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These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:

Data Citation

Kiser, K.J., Ahmed, S. M. E. H., 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 for symmetry-sensitive deep learning architecture development [Data set]. The Cancer Imaging Archive. https://doi.org/DOI goes here.

Acknowledgement


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. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

Other Publications Using This Data

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

Version 1 (Current): Updated 2020/03/XX

Data TypeDownload all or Query/Filter
Images (NIfTI, xx.x GB)

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Supplemental Data (CSV)


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