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  • A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis (Lung-PET-CT-Dx)

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Summary


Our dataset consists of: DICOM files and XML Annotation files.  The XML Annotation files, which are widely used in deep learning and machine learning research, were provided by five Radiologist (original said doctors) and two deep learning researchers making our dataset a useful tool and resource for developing algorithms for medical diagnosis.

The images were analyzed on the mediastinum (window width, 350 HU; level, 40 HU) and lung (window width, 1,400 HU; level, –700 HU) settings. The reconstructions were made in 2mm-slice-thick and lung settings. The CT slice interval varies from 0.625 mm to 5 mm. Scanning mode includes plain and contrast and 3D reconstruction.  The location of the tumors was labeled in the DICOM images, and the image annotations are saved in XML files in PASCAL VOC format. Users can parse the annotations using the PASCAL Development Toolkit  https://pypi.org/project/pascal-voc-tools/

Several lines about the protocol and how subjects were recruited.  The cases were confirmed by pathological diagnosis.


Acknowledgements

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

  • Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.

  • Continue with any names from additional submitting sites if collection consists of more that one.

Data Access

Data TypeDownload all or Query/Filter
Images (DICOM, XX.X GB)

(Download requires the NBIA Data Retriever)

Annotation Files (XML) 14.62 (MB)

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Please contact help@cancerimagingarchive.net  with any questions regarding usage.

Detailed Description

Image Statistics


Modalities


Number of Patients


Number of Studies


Number of Series


Number of Images


Images Size (GB)

Add any additional information as needed below. Likely would be something from site.


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 4.0 International 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

DOI goes here. Create using Datacite with information from Collection Approval form

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


In addition to the dataset citations above, please be sure to cite the following if you utilize these data in your research:

Acknowledgement


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 yyyy/mm/dd

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

(Requires NBIA Data Retriever.)

Annotation Files (XML) 14.62 (MB)

Clinical Data (CSV)Link
Other (format)



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