Summary


This collection contains a labelled dataset of MRI images collected on 242 consecutive patients with vestibular schwannoma (VS) undergoing Gamma Knife stereotactic radiosurgery (GK SRS). The structural images included contrast-enhanced T1-weighted (ceT1) images and high-resolution T2-weighted (hrT2) images. Each imaging dataset is accompanied by the patient’s radiation therapy (RT) dataset including the RTDose, RTStructures and RTPlan. All structures were manually segmented in consensus by the treating neurosurgeon and physicist using both the ceT1 and hrT2 images. The value of this collection is to provide clinical image data including fully annotated tumour segmentations to facilitate the development and validation of automated segmentation frameworks. It may also be used for research relating to radiation treatment.

Imaging data from consecutive patients with a single sporadic VS treated with GK SRS between the years of 2012 and 2018 were screened for the study. All adult patients older than 18 years with a single unilateral VS treated with GK SRS were eligible for inclusion in the study, including patients who had previously undergone operative surgical treatment. In total, 248 patients met these initial inclusion criteria including 51 patients who had previously undergone surgery. Patients were only included in the study if their pre-treatment image acquisition dataset was complete; 2 patients were thus excluded because of incomplete datasets.

The images were obtained on a 32-channel Siemens Avanto 1.5T scanner using a Siemens single-channel head coil.

  • Contrast-enhanced T1-weighted imaging was performed with an MPRAGE sequence with TR / TE / TI = 1900 ms / 2.97 ms / 1100 ms, in-plane resolution of 0.4 × 0.4 mm, in-plane matrix of 512 × 512, and slice thickness of 1.0–1.5 mm
  • High-resolution T2-weighted imaging was performed with a 3D CISS or FIESTA sequence with TR / TE 9.4 ms / 4.23 ms, in-plane resolution of approximately 0.5 × 0.5 mm, an in-plane matrix of 384 × 384 or 448 × 448, and slice thickness of 1.0–1.5 mm.

All manual segmentations were performed using Gamma Knife planning software (Leksell GammaPlan, Elekta, Sweden) that employs an in-plane semiautomated segmentation method. Using this software, each axial slice was manually segmented in turn.

Acknowledgements


Data Access

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Detailed Description

Image Statistics


Modalities

MR, RTSTRUCT, RTDOSE, RTPLAN

Number of Participants

242

Number of Studies

242

Number of Series

1936

Number of Images

48582

Images Size (GB)



Citations & Data Usage Policy


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.​


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: < https://doi.org/10.3171/2019.9.JNS191949>


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: https://doi.org/10.1007/s10278-013-9622-7


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).

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Images, Segmentations, and Radiation Therapy Structures (DICOM, xx.x GB)

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