Localtab |
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active | true |
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title | Data Access |
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| Data AccessClick the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever
Data Type | Download all or Query/Filter |
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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. |
Localtab |
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title | Detailed Description |
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| Detailed DescriptionData resulting from this experiment is available in the following formats: - DICOM image format
- Processed NIFTI images with segmentations and radiomic features
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Localtab |
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy Public collection license |
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Info |
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| 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 |
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title | Publication Citation |
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| 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
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| 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 DataTCIA 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
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Localtab |
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| Version 1 (Current): 2017/07/17
Data Type | Download all or Query/Filter |
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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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