A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis

Summary

Our dataset consists of three parts: raw DICOM data, JPG images transformed from raw DICOM data, and non-image data including sex, age, history, some patients have gene expression, and pathologist reports. The images were analyzed both 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 include plain scan, contrast scan, 3D reconstruction, etc. All the cases were confirmed by pathological diagnosis. We labeled the locations of tumor in JPG images. And the image annotations are saved in XML files in Annotation Files with Hashed Filenames format. Users can parse the annotations using the PASCAL Development Toolkit.

We provide JPG images and XML annotation files in PASCAL VOC format which is widely used in deep learning and machine learning researches. The annotation files are provided by five doctors and two deep learning researchers. Besides that, all the cases were confirmed by pathology. Thus, we can guarantee our dataset precise and ease of use. Our dataset can be regarded as a useful tools and data resource to develop medical diagnosis algorithm based on deep learning. On the other hand, our data set can be used as an effective tool for promoting medical diagnosis.

Acknowledgements

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Data Access

Data Type	Download 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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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

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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

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Acknowledgement

Other Publications Using This Data

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Version 1 (Current): Updated yyyy/mm/dd

Data Type	Download all or Query/Filter
Images (DICOM, xx.x GB)	(Requires NBIA Data Retriever.)
Annotation Files (XML) 14.62 (MB)
Clinical Data (CSV)	Link
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