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Summary

These MRIs are pre-operative examinations performed in subjects with Low Grade Gliomas (WHO grade II & III). All of these subjects have biopsy proven 1p19q results, performed using FISH. Segmentation of tumors in three axial slices that include the one with the largest tumor diameter and ones below and above also provided in NiFTI format.

 

Data Access

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Data TypeDownload all or Query/Filter
Images (DICOM, 2.7GB)

 

Segmentations (NiFTI, 2.9GB)

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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).

 

Citations & Data Usage Policy 

This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License.  See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net.

Please be sure to include the following citations in your work if you use this data set:

Data Citation

Erickson, Bradley; Akkus, Zeynettin; Sedlar, Jiri; Korfiatis, Panagiotis. (2017). Data From LGG-1p19qDeletion. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2017.dwehtz9v

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. (paper)

 

Other Publications Using This Data

  1.  Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence. Zeynettin AkkusIssa AliJiří SedlářJay P. AgrawalIan F. ParneyCaterina Giannini,and Bradley J. Erickson. J Digit Imaging. 2017 Aug; 30(4): 469–476. Published online 2017 Jun 9.  doi:  10.1007/s10278-017-9984-3. PMCID: PMC5537096
  2. 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.


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

Version 1 (Current): Updated 2017/09/30

Data TypeDownload all or Query/Filter
Images (2.7GB)

 

Segmentations (NiFTi, 2.9GB)
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