The dataset should not be listed until a related manuscript is published.
Martin Vallières et al. (2017) MRI features predict survival and molecular markers in diffuse lower-grade gliomas. The Cancer Imaging Archive. https://doi.org/link
To perform this study, we have contoured the LGG tumour region on T1-weighted, T2-weighted, T1-weighted post-contrast and T2-flair images for a subset of the TCGA-LGG patients (total of 108). The end results is a set of ROI masks for each imaging series in MATLAB format. We would like now to share these ROI masks on the TCIA website.
In order to share this data on the TCIA website, we have created a new set of DICOM images containing only 0's and 1's to define the ROI in the corresponding DICOM images of the 108 TCGA-LGG patients. We understand that this is definitely not the most efficient way of sharing ROIs (I see that DICOM SEG format is becoming the accepted standard?). However, this is the best that we have at the moment.
We would like to know if it is possible to share those ROI masks in DICOM format (~15GB)? Another option would be to directly share the ROI masks in MATLAB format (~130MB). Otherwise, do you have another option to suggest?
Finally, my collaborators would like to add a restriction on the access of the ROI masks (i.e. asking permission by email), but only for a certain period of time.
MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro-Oncology. [Epub ahead of print] PMID:
- (xx GB)
- Processed NIFTI images + Segmentations (xx MB)
- Additional Processed NIFTI images + Segmentations used in BRATS 2017 Test Data Set (xx MB) – (available by email request only until completion of Challenge)