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  • Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection (BraTS-TCGA-LGG)
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Summary

This data container describes both computer-aided and manually-corrected segmentation labels for the pre-operative multi-institutional scans of The Cancer Genome Atlas (TCGA) Low Grade Glioma (LGG) collection, publicly available in The Cancer Imaging Archive (TCIA), coupled with a rich panel of radiomic features along with their corresponding skull-stripped and co-registered multimodal (i.e. T1, T1-Gd, T2, T2-FLAIR) magnetic resonance imaging (MRI) volumes in NIfTI format. Pre-operative multimodal MRI scans were identified in the TCGA-LGG collection via radiological assessment. These scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated hybrid generative-discriminative method, ranked first during the International multimodal BRAin Tumor Segmentation challenge (BRATS 2015). These segmentation labels were revised and any label misclassifications were manually corrected by an expert board-certified neuroradiologist. The final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters, as well as spatial information and diffusion properties extracted from glioma growth models. The generated computer-aided and manually-revised labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. The provided panel of radiomic features may facilitate research integrative of the molecular characterization offered by TCGA, and hence allow associations with molecular markers, clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features.


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 - 108 Subjects (DICOM, 8.5 GB)
Processed images with segmentations and radiomic features - 65 subjects (NIFTI, 536 MB)
BRATS 2018 Test Data Set - 43 subjects (NIFTI, 366 MB)

Please contact the  helpdesk  to request access to these files.

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

Collections Used in this Third Party Analysis
Below is a list of the Collections used in these analyses:

Detailed Description

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

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

Citations & Data Usage Policy 

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. Attribution should include references to the following citations:

Data Citation

Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C. (2017) Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection [Data Set]. The Cancer Imaging Archive. DOI:  10.7937/K9/TCIA.2017.GJQ7R0EF

Publication 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

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. T he 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

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.

  • Aliotta, E., Dutta, S. W., Feng, X., Tustison, N. J., Batchala, P. P., Schiff, D., . . . Patel, S. H. (2020). Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign. J Neurooncol, 149(2), 325-335. doi:https://doi.org/10.1007/s11060-020-03611-8
  • 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.
  • Ge, C. (2020). Machine Learning Methods for Image Analysis in Medical Applications From Alzheimer’s Disease, Brain Tumors, to Assisted Living. (Ph. D. Dissertation), Chalmers University of Technology, Göteborg, Sweden. Retrieved from https://research.chalmers.se/publication/517576 10.7937/K9/TCIA.2017.KLXWJJ1Q; 10.7937/K9/TCIA.2017.GJQ7R0EF database.
  • Rafi, A., Ali, J., Akram, T., Fiaz, K., Raza Shahid, A., Raza, B., & Mustafa Madni, T. (2020, March 18-19, 2020). U-Net Based Glioblastoma Segmentation with Patient’s Overall Survival Prediction. Paper presented at the ISICS: International Symposium on Intelligent Computing Systems, Sharjah, United Arab Emirates.
  • 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
  • Tunga, P. (2019). Extraction of Tumor in Brain MRI using Support Vector Machine and Performance Evaluation. Visvesvaraya Technological University Journal of Engineering Sciences and Management, 1(3), 1-8.
  • Zhang, X., Liu, S., Zhao, X., Shi, X., Li, J., Guo, J., . . . Zhang, X. (2020). Magnetic resonance imaging-based radiomic features for extrapolating infiltration levels of immune cells in lower-grade gliomas. Strahlentherapie und Onkologie, 196(10), 913-921. doi:10.1007/s00066-020-01584-1

Version 1 (Current): 2017/07/17

Data TypeDownload all or Query/Filter
Images - 108 subjects (DICOM, 8.5 GB)
Processed images with segmentations and radiomic features - 65 subjects (NIFTI, 536 MB)
BRATS 2018 Test Data Set - 43 subjects (NIFTI, 366 MB)

Please contact the  helpdesk  to request access to these files.

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