Contains both high and low grade galial brain lesions.
- Directories
Directories 01 - 16 include pairs of T1 MRI Scans and Masks.
- supplementary.csv
CSV file that gives additional information for the data, in this case just the date the scan was taken. This file may be used by
process.py.- process.py
Python script used to modify original dataset into this dataset, contains useful helper functions to use in the interpreter or script to make further modifications. This file is messy and unneeded but gives a technical description of the changes made to supplement the explanation below.
- metadata.csv
The metadata file from the TCIA downloader showing the raw data this dataset is based on/downloaded from.
- licence.html
The licence file from the TCIA downloader. This is the original licence file that is included when the data is downloaded from the source.
- README.rst
This document
This repository includes data from the Brain-Tumor-Progression collection published by The Cancer Imaging Archive. The original data has been modified in the following ways: Firstly all DICM image slices per set have been combined into one Gzipped NIfTI file, renaming the resulting NIfTI images Pre.... and Post... for the first and second scan respectivly, for both the MRI Scans and associated tumor masks resulting in four .nii.gz images. The images have all been resized to 256 X 256 using Nearest Neighbors interpolation and the number of slices was first reduces to 22, for some sets that required a few slides to be dropped and resulted in a closer relationship between Pre and Post slices for the same slice number, for others no change was required. Following that the slices were then padded with blank slides to bring the final count to 24 slices each; this was done so that three subsample convolutional layers could be composed without ending up with decimal dimensions (24 % 2^3 = 0).
Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 3.0 Unported License under which it has been published. Any publications discussing these data should include references to the following:
- Data Citation
Schmainda KM, Prah M (2018). Data from Brain-Tumor-Progression. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2018.15quzvnb
Kathleen M Schmainda, Melissa A Prah, Jennifer M Connelly, Scott D Rand. (2016). Glioma DSC-MRI Perfusion Data with Standard Imaging and ROIs [ Dataset ] . The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2016.5DI84Js8
- Publication Citation
Schmainda KM, Prah MA, Rand SD, Liu Y, Logan B, Muzi M, Rane SD, Da X, Yen YF, Kalpathy-Cramer J, Chenevert TL, Hoff B, Ross B, Cao Y, Aryal MP, Erickson B, Korfiatis P, Dondlinger T, Bell L, Hu L, Kinahan PE, Quarles CC. (2018). Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project. American Journal of Neuroradiology, 39(6), 1008–1016. DOI: 10.3174/ajnr.a5675
- 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. https://doi.org/10.1007/s10278-013-9622-7