Segment3DBrainTumorUsingDeepLearningExample/TRAININGDATA at master · ImageGuidedTherapyLab/Segment3DBrainTumorUsingDeepLearningExample · GitHub

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## Skull Stripping
===============

**Neurofeedback Skull-stripped (NFBS) repository**

/rsrch1/ip/egates1/NFBS\ Skull\ Strip/NFBSFilepaths.csv. File paths are absolute for the time being.

Data downloaded from http://preprocessed-connectomes-project.org/NFB_skullstripped and consists of 125 high-resolution T1w images (de-faced) with ground truth brain masks.

Citation:
https://pubmed.ncbi.nlm.nih.gov/27782853/


## Tumor Segmentation
===============

**MICCAI 2018 Brain Tumor Segmentation Challenge**

/rsrch1/ip/egates1/MICCAI_BraTS_2018_Data_Training/BraTS18_filepaths_train_val_tst.csv.
The paths are relative to the MICCAI_BraTS_2018_Data_Training directory

Training data contains 210 high grade glioma (HGG cases) and 75 low-grade glioma (LGG) cases. 163 of the HGG cases also have overall survival in days.

Each case has T1, T2, FLAIR, T1CE, and seg (for training data) as provided by the challenge organizers https://www.med.upenn.edu/sbia/brats2018.html. Labels are: enhancing tumor (4), peritumoral edema (2), and the necrosis/non-enhancing tumor (1). Input images have been rigidly co-registered, skull stripped, and resampled to 1mm isotropic resolution.

A few auxiliary files have been added for each case: mask (all non-zero pixels), tumor (union of ground truth segmentation labels), and nontumor (brain – tumor).

Copied directly from the challenge website:

You are free to use and/or refer to the BraTS datasets in your own research, provided that you always cite the following three manuscripts:

[1] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694

[2] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI: 10.1038/sdata.2017.117

[3] S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al., "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge", arXiv preprint arXiv:1811.02629 (2018)

In addition, if there are no restrictions imposed from the journal/conference you submit your paper about citing "Data Citations", please be specific and also cite the following:

[4] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., "Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection", The Cancer Imaging Archive, 2017. DOI: 10.7937/K9/TCIA.2017.KLXWJJ1Q

[5] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., "Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection", The Cancer Imaging Archive, 2017. DOI: 10.7937/K9/TCIA.2017.GJQ7R0EF