Localtab |
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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 - 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. Collections Used in this Third Party Analysis Below is a list of the Collections used in these analyses: |
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 for the Pre-operative Scans of the TCGA-GBM collection [Data set]. The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2017.KLXWJJ1Q |
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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. 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 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. - 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
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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 - 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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