Summary
This multi-center routine clinical (MC-RC) dataset consists of 165 patients with a single sporadic Vestibular Schwannoma (VS) who were referred from 10 medical sites and consecutively seen at a single center. These routine clinical datasets are more diverse in terms of the tumor manifestation as well as the acquisition parameters. Using this dataset, researchers can develop and validate methods for automatic surveillance of Vestibular Schwannoma, which work robustly on images acquired at different hospitals.
Patients had multiple time points resulting in a total of 446 timepoints and 509 3D-images. Manual ground truth segmentations were obtained in an iterative process in which segmentations were: 1) produced or amended by a specialized company; and 2) reviewed by one of three trained radiologists; and 3) validated by an expert team. Compared to the existing Vestibular-Schwannoma-SEG dataset on TCIA that was obtained from a single scanner, this dataset was acquired from multiple scanners manufactured by different vendors. This dataset also provides a refined segmentation of intrameatal and extrameatal components of the VS.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
This work was supported by Wellcome Trust (203145Z/16/Z, 203148/Z/16/Z, WT106882), EPSRC (NS/A000050/1, NS/A000049/1) and MRC (MC/PC/180520) funding. Additional funding was provided by Medtronic. TV is also supported by a Medtronic/Royal Academy of Engineering Research Chair (RCSRF1819/7/34).
Data Access
This is a limited access data set. To request access please register an account on the NCTN Data Archive. After logging in, use the "Request Data" link in the left side menu. Follow the on screen instructions, and enter NCT00352534 when asked which trial you want to request. In step 2 of the Create Request form, be sure to select “Imaging Data Requested”. Please contact NCINCTNDataArchive@mail.nih.gov for any questions about access requests.
| Data Type | Download all or Query/Filter | License |
|---|---|---|
Images, Segmentations, and Radiation Therapy Structures/Doses/Plans (DICOM, XX.X GB) << latter two items only if DICOM SEG/RTSTRUCT/RTDOSE/PLAN exist >> | (Download requires NBIA Data Retriever) | |
| Tissue Slide Images (SVS, XX.X GB) | (Download requires Aspera plugin) | |
| Clinical data (CSV) |
Click the Versions tab for more info about data releases.
Detailed Description
Image Statistics | Radiology Image Statistics | Pathology Image Statistics |
|---|---|---|
Modalities | ||
Number of Patients | 165 | |
Number of Studies | ||
Number of Series | ||
Number of Images | ||
| Images Size (GB) |
<< Add any additional information that didn't fit or belong in the Summary section. >>
Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Data Citation
DOI goes here. Create using Datacite with information from Collection Approval form
Publication Citation
Kujawa, A., Dorent, R., Connor, S., Thomson, S., Ivory, M., Vahedi, A., Guilhem, E., Bradford, R., Kitchen, N., Bisdas, S. and Ourselin, S., 2022. Deep Learning for Automatic Segmentation of Vestibular Schwannoma: A Retrospective Study from Multi-Centre Routine MRI. medRxiv. Wijethilake, N., Kujawa, A., Dorent, R., Asad, M., Oviedova, A., Vercauteren, T. and Shapey, J., 2022. Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma. In International Workshop on Machine Learning in Clinical Neuroimaging (pp. 73-82). Springer, Cham.
Acknowledgement
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.
TCIA Citation
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 Data
TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.
Version X (Current): Updated yyyy/mm/dd
| Data Type | Download all or Query/Filter | License |
|---|---|---|
Images, Segmentations, and Radiation Therapy Structures/Doses/Plans (DICOM, XX.X GB) << latter two items only if DICOM SEG/RTSTRUCT/RTDOSE/PLAN exist >> | (Download requires the NBIA Data Retriever) | |
| Tissue Slide Images (SVS, XX.X GB) | ||
| Clinical data (CSV) |
<< One or two sentences about what you changed since last version. No note required for version 1. >>