In addition to the DICOM images, this collection also contains.
This data set was provided to TCIA by Authors: Bradley Erickson, Zeynettin Akkus, Jiri Sedlar, Panagiotis Korfiatis.
Data Access
Choosing the Download option will provide you with a file to launch the TCIA Download Manager to download the entire collection. If you want to browse or filter the data to select only specific scans/studies please use the Search By Collection option.
Data Type
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Images (DICOM, XXX GB)
Segmentations (NiFTI)
Click the Versions tab for more info about data releases.
Detailed Description
Collection Statistics
Updated 2017/07/31
Modalities
MRI, SEG, NIfTI
Number of Patients
159
Number of Studies
160
Number of Series
319
Number of Images
17360
Image Size (GB)
2.7
Supporting Documentation and Metadata
Acquisition parameters for this Collection: Pre-operative post-biopsy MRI images of brain (DICOM format), segmentations (NIfTI format), and 1p-19q co-deletion data (text files).
Please be sure to include the following citations in your work if you use this data set:
Authors. (2017). Data From LGG 1p19q Deletion. The Cancer Imaging Archive. http://doi.org
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. (paper)
Other Publications Using This Data
https://arxiv.org/abs/1611.06939 Zeynettin Akkus, Issa Ali, Jiri Sedlar, Timothy L. Kline, Jay P. Agrawal, Ian F. Parney, Caterina Giannini, Bradley J. Erickson. Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning. (2016).
https://doi.org/10.1007/s10278-017-9965-6 Bradley J. Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy Kline, Kenneth Philbrick. Toolkits and Libraries for Deep Learning. Journal of Digital Imaging 2017 p1618-1627.