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  • Site to provide acknowledgements including grant information.

Data Access

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Images, Segmentations, and Radiation Therapy Structures (DICOM, XX.X GB)


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Detailed Description

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Citations & Data Usage Policy

Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 4.0 International License under which it has been published. Attribution should include references to the following citations:

Data Citation

Shapey, J., Kujawa, A., Dorent, R., Wang, G., Bisdas, S., Dimitriadis, A., Grishchuck, D., Paddick, I., Kitchen, N., Bradford, R., Saeed, S., Ourselin, S., Vercauteren, T. (2020). Segmentation of vestibular schwannoma from magnetic resonance imaging: An open annotated dataset and baseline algorithm (Vestibular-Schwannoma-SEG) [Data Set]. The Cancer Imaging Archive. DOI Pending.​

Publication Citation

Shapey, J., Wang, G., Dorent, R., Dimitriadis, A., Li, W., Paddick, I., Kitchen, N., Bisdas, S., Saeed, S. R., Ourselin, S., Bradford, R., and Vercauteren, T. (2019). An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI. Journal of Neurosurgery JNS , 1-9, available from: <>

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. 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:


This work was supported by Wellcome Trust (203145Z/16/Z, 203148/Z/16/Z, WT106882) and EPSRC (NS/A000050/1, NS/A000049/1) funding. Tom Vercauteren is also supported by a Medtronic/Royal Academy of Engineering Research Chair (RCSRF1819\7\34).

Other Publications Using This Data

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Version 1 (Current): Updated yyyy/mm/dd

Data TypeDownload all or Query/Filter
Images, Segmentations, and Radiation Therapy Structures (DICOM, xx.x GB)

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