TCIA DOIs
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  • Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection (BraTS-TCGA-GBM)

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Comment: updated derived citations

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Localtab Group



Localtab
activetrue
titleData Access

Data Access

Click the Download  button to save a ".tcia" manifest file to your computer, which you must open with the  NBIA Data Retriever


Data TypeDownload all or Query/Filter
Images - 135 subjects (DICOM, 6 GB)

Processed NIFTI images with segmentations and radiomic features - 102 subjects (NIFTI, 767 MB)

BRATS 2018 Test Data Set - 33 subjects (NIFTI, 255 MB)

Please contact the helpdesk to request access to these files.


Note:   Please contact help@cancerimagingarchive.net  with any questions regarding usage.




Localtab
titleDetailed Description

Detailed Description

Data resulting from this experiment is available in the following formats:

  • DICOM image format
  • Processed NIFTI images with segmentations and radiomic features




Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

Public collection license

Info
titleData Citation

Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C. (2017). Segmentation Labels for the Pre-operative Scans of the TCGA-GBM collection [Data set]. The Cancer Imaging Archive. DOI:  10.7937/K9/TCIA.2017.KLXWJJ1Q


Info
titlePublication Citation

Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C. (2017) Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features Nature Scientific Data, 4:170117 DOI:  10.1038/sdata.2017.117  


Info
titleTCIA 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


Other Publications Using This Data

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

  • Astaraki, M., Wang, C., Carrizo, G., Toma-Dasu, I., & Smedby, Ö. (2020). Multimodal Brain Tumor Segmentation with Normal Appearance Autoencoder. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Shenzhen, China.
  • Bhadani, S., Mitra, S., & Banerjee, S. (2020). Fuzzy volumetric delineation of brain tumor and survival prediction. Soft Computing, 24(17), 13115-13134. doi:10.1007/s00500-020-04728-8
    Chan, H.-W., Weng, Y.-T., & Huang, T.-Y. (2020). Automatic Classification of Brain Tumor Types with the MRI Scans and Histopathology Images. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries Shenzhen, China,.
  • Chen, M., Wu, Y., & Wu, J. (2020). Aggregating Multi-scale Prediction Based on 3D U-Net in Brain Tumor Segmentation. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. , Shenzhen, China.
  • Han, W.-S., & Han, I. S. (2020, October 2019). Multimodal Brain Image Segmentation and Analysis with Neuromorphic Attention-Based Learning. Paper presented at the International MICCAI Brainlesion Workshop, Shenzhen, China.
  • Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., . . . Bakas, S. (2020). Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep, 10(1), 12598. doi:https://doi.org/10.1038/s41598-020-69250-1
  • Thakur, S., Doshi, J., Pati, S., Rathore, S., Sako, C., Bilello, M., . . . Bakas, S. (2020). Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training. Neuroimage, 220, 117081. doi:https://doi.org/10.1016/j.neuroimage.2020.117081




Localtab
titleVersions

Version 1 (Current): 2017/07/17


Data TypeDownload all or Query/Filter
Images - 135 subjects (DICOM, 6 GB)

Processed NIFTI images with segmentations and radiomic features - 102 subjects (NIFTI, 767 MB)

BRATS 2018 Test Data Set - 33 subjects (NIFTI, 255 MB)

Please contact the helpdesk to request access to these files.





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