Localtab |
---|
active | true |
---|
title | Data Access |
---|
| Data AccessData Type | Download all or Query/Filter | License |
---|
Processed NIFTI images with segmentations and radiomic features - 102 subjects (NIFTI, 767 MB) |
Tcia button generator |
---|
url | https://faspex.cancerimagingarchive.net/aspera/faspex/external_deliveries/88?passcode=f800fe81c0e92dbc612be70b91d505e9a511ce0b |
---|
|
|
(Download and apply the IBM-Aspera-Connect plugin to your browser.) | | BRATS 2018 Test Data Set - 33 subjects (NIFTI, 255 MB) | Please contact the helpdesk to request access to these files. | |
Click the Versions tab for more info about data releases. Collections Used in this Third Party Analysis Below is a list of the Collections used in these analyses:
Source Data Type | Download all or Query/Filter | License |
---|
Corresponding Original Images from TCGA-GBM - 135 subjects (DICOM, 6 GB) |
Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/download/attachments/24282666/doiJNLP-QoOaKUdn.tcia?api=v2 |
---|
|
|
(Requires NBIA Data Retriever.) | |
Please contact help@cancerimagingarchive.net with any questions regarding usage. |
Localtab |
---|
title | Detailed Description |
---|
| Detailed DescriptionData resulting from this experiment is available in the following formats: - DICOM image format
- Processed NIFTI images with segmentations and radiomic features
|
Localtab |
---|
title | Citations & Data Usage Policy |
---|
| Citations & Data Usage Policy Tcia limited license policy |
---|
Info |
---|
| 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 |
---|
title | 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 |
Info |
---|
| 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 DataThakur, 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.2020.117081TCIA maintainsmaintains a list of publications that which leverage TCIA data. If you have a manuscript publications 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's Helpdesk.
|
Localtab |
---|
| Version 1 (Current): 2017/07/17
Data Type | Download all or Query/Filter |
---|
Images - 135 subjects (DICOM, 6 GB) | Note: This collection contains data that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Limited Access License to help@cancerimagingarchive.net before accessing the data. | Processed NIFTI images with segmentations and radiomic features - 102 subjects (NIFTI, 767 MB) | Download and apply the IBM-Aspera-Connect plugin to your browser to access the data. | BRATS 2018 Test Data Set - 33 subjects (NIFTI, 255 MB) | Please contact the helpdesk to request access to these files. |
|
|