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title | Data Access |
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| Data Access Tcia license 4 international |
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Note to curators! This macro is for collections that are restricted due to facial reconstruction possibility.
Data Type | Download all or Query/Filter | License |
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Images and Segmentations (NIfTI, 1.4 TB)
| Complete dataset Tcia button generator |
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complete data on faspex |
(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) | | BraTS 2021 Training set in batches of XXX PatientID Images, Segmentations (NIfTI, 1.4 TB) from CPTAC
| batch1 (ID PP to QQ) (XX GB) Tcia button generator |
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batch1 |
batch2 (ID XX to YY) (XX GB) Tcia button generator |
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batch2 |
(and so on)
(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package)
| | BraTS 2021 Validation set in batches of XXX PatientID Images, Segmentations (NIfTI, 1.4 TB) |
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batch1 |
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batch2 by case ID |
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NIfTI derived from Ivy GAP sources |
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NIfTI derived from CPTAC-GBM sources |
(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package)
| | BraTS Task2 Radiogenomics Classifier task images (DICOM?, GB) | - link to faspex for nifti
- link to GDC/PDC if the detail are there
| ( is this open license stuff?) | BraTS Task2 Radiogenomics Classifier task molecular marker table (JSON/XLS/CSV spreadsheet, MB) | - download JSON/XLS/CSV spreadsheet or
- if external link that would go below not in this table
| ( is this open license stuff?) | Images from TCGA-LGG that have been transformed for use in this challenge - 108 Subjects (DICOM, 8.5 GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/24282668/doiJNLP-JAMS4RFq.tcia?api=v2 |
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| TCGA-LGG batch1 source series |
Note: Limited Access. Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever . | | Images from TCGA-GBM that have been transformed for use in this challenge- 135 subjects (DICOM, 6 GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/24282666/doiJNLP-QoOaKUdn.tcia?api=v2 |
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| TCGA-GBM batch1 source series |
Note: Limited Access. Click the Download button to save a ".tcia" manifest file, needs the NBIA Data Retriever | | Images from Ivy GAP that have been transformed for use in this challenge- XXX subjects (DICOM, XX GB) |
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Ivy GAP batch1 source series |
Note: Limited Access. Click the Download button to save a ".tcia" manifest file, needs the NBIA Data Retriever | | CPTAC-GBM DICOM | | | UCSF-PDGM DICOM (note Evan didn't give this to us – only nifti so far) | | | UPENN-GBM DICOM ( others) | | | Transformation matrices DICOM to NII (zip, XXMB) | | | Clinical data (CSV)
| | | Feature matrices (format, ##GB) | | |
Click the Versions tab for more info about data releases. Additional Resources for this DatasetNote to curators! Use this any time you are linking to NCI's IDC/GDC/PDC resources. The links below are examples and will need to be tailored to point to the specific dataset (see parameters in URLS). Nci_crdc additional resources |
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Note to curators! Below are examples for what to do with other external resources/links that don't fit into the above categories. The following external resources have been made available by the data submitters. These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection. - Software / Code on Github
- Genomics data in DbGAP
- Genomics data in Gene Expression Omnibus
- RSNA or kaggle link to molecular marker table if they only provide us a link
TCIA Collections Used (in part) in these analyses:Third Party Analyses of this DatasetTCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection: - <these get filled in as groups cite the dataset in their papers related to analysis of the 2021 task 1 & task2 using this Collection's DOI>
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title | Detailed Description |
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| Detailed DescriptionImage Statistics | Radiology Image Statistics | Pathology Image Statistics |
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- the "challenge test set dataset" is sequestered on synapse as a tarball of nii & seg & age & diagnosis. Not available. Please see <website> for more detail.
The data used in BraTS Challenges often have some overlap with other TCIA Collections, cases, and series. Some filters for handling these, so that you can work with statistically not-duplicated images, include these below: - Manifest of case identifiers between BraTS and TCIA
- Spreadsheet list of cases and series used in prior year BraTS Challenges which may refer also to these:
- Spreadsheet list of new (DICOM and NIFTI) series files with no TCIA DICOM equivalent:
- Manifest of (DICOM and NIFTI) files sourced from other TCIA Collections, so you can avoid superset accidental duplication in case you want everything TCIA has, after you've gotten all of BRaTS data:
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy Tcia limited license policy |
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| DOI goes here. Create using Datacite with information from Collection Approval form 44 authors, most with ORCiD. |
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title | Publication Citation |
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| U.Baid, et al., "The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification", arXiv:2107.02314, 2021. |
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| Required acknowledgements only (ex:The CPTAC program requests that publications using data from this program...). If they just want to thank someone, that goes in the Acknowledgement section underneath the Summary."The results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/."
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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. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC.https://doi.org/10.1007/s10278-013-9622-7 |
Other Publications Using This DataTCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk. Copied from the Kaggle site: You are free to use and/or refer to the BraTS datasets in your own research, provided that you always cite the flagship manuscript (published or pre-published) resulting from the challenge as well as the following three manuscripts: - U.Baid, et al., The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification, arXiv:2107.02314, 2021.
- Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.-A., Arbel, T., Avants, B. B., Ayache, N., Buendia, P., Collins, D. L., Cordier, N., … Van Leemput, K. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). In IEEE Transactions on Medical Imaging (Vol. 34, Issue 10, pp. 1993–2024). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tmi.2014.2377694
- Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017). Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. In Scientific Data (Vol. 4, Issue 1). https://doi.org/10.1038/sdata.2017.117
In addition, please be specific and also cite the following datasets that were part of this Challenge:- 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. https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
- 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-LGG collection [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
- Scarpace, L., Mikkelsen, T., Cha, S., Rao, S., Tekchandani, S., Gutman, D., Saltz, J. H., Erickson, B. J., Pedano, N., Flanders, A. E., Barnholtz-Sloan, J., Ostrom, Q., Barboriak, D., & Pierce, L. J. (2016). The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM) (Version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.RNYFUYE9
- Pedano, N., Flanders, A. E., Scarpace, L., Mikkelsen, T., Eschbacher, J. M., Hermes, B., Sisneros, V., Barnholtz-Sloan, J., & Ostrom, Q. (2016). The Cancer Genome Atlas Low Grade Glioma Collection (TCGA-LGG) (Version 3) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.L4LTD3TK
- Calabrese, E., Villanueva-Meyer, J., Rudie, J., Rauschecker, A., Baid, U., Bakas, S., Cha, S., Mongan, J., & Hess, C. (2022). The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) [Dataset]. The Cancer Imaging Archive. https://10.7937/tcia.bdgf-8v37
- Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J. D., Flores Santamaria, N., Fathi Kazerooni, A., Pati, S., Rathore, S., Mamourian, E., Ha, S. M., Parker, W., Doshi, J., Baid, U., Bergman, M., Binder, Z. A., Verma, R., … Davatzikos, C. (2021). Multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the University of Pennsylvania Health System (UPENN-GBM) (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.709X-DN49
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| Version X (Current): Updated yyyy/mm/ddcopy Access tab table here << One or two sentences about what you changed since last version. No note required for version 1. >> |
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