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Summary


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.

Thoracic segmentations were generated automatically by a U-Net, manually corrected by a medical student, and revised by a radiation oncologist or a radiologist. 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. The thoracic segmentations were developed to study image feature symmetry between left and right thorax anatomy as a predictor for tumor localization. Previous studies have developed novel deep learning architectures that leverage deviations from natural anatomic symmetry to predict endpoints. For example, a deep learning architecture called DeepSymNet localized stroke cores in brains by comparing information acquired from convolutional neural networks that analyzed brain hemispheres. Researchers interested in developing novel deep learning architectures that predict NSCLC endpoints using a similar methodology may find our thoracic segmentations useful. Researchers interested in discriminating between tumor 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.

This dataset was utilized in a clinical informatics study correlating autosegmentation accuracy as gauged by several common metrics with time required for manual corrections. Manual correction time, accuracy metrics, and other tabular data are provided and extend the tabular data accompanying the NSCLC Radiomics CT datasets.


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

Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever. Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.

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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.

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., 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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