Open dataset · MR

Vestibular-Schwannoma-MC-RC - routine multi-center MR

160 vestibular schwannoma patients, 427 timepoints and 487 routine clinical MR DICOM series from 10 UK medical sites.

The sample cases below open read-only in the MedSeg editor - no account needed. Open the full dataset to copy all 487 cases into your workspace, run AI models, and segment.

MR 487 cases CC-BY-4.0 Reference masks Pathology
Vestibular-Schwannoma-MC-RC - routine multi-center MR - example case rendered in the MedSeg editor
Example case with its reference segmentation - straight from the catalog, rendered by MedSeg.

Browse sample cases

These cases open instantly in the browser-based editor - scroll the slices, inspect the reference masks, window the image. No account needed.

Open the full dataset in MedSeg → All 487 cases - copy into your workspace to run AI and segment.

About this dataset

Vestibular-Schwannoma-MC-RC is a multi-center routine clinical MRI dataset for unilateral sporadic vestibular schwannoma. It contains longitudinal scans from 160 patients across 10 UK medical sites, with up to three selected timepoints per patient. Images are provided as defaced MR DICOM and retain heterogeneous clinical acquisition protocols across Siemens, Philips, GE, and Hitachi scanners. The source paper reports screened imaging dates from February 2006 to September 2019; TCIA study dates are shifted for privacy.

MedSeg imports all public MR image series. Reference masks are attached where the public 2023 NIfTI segmentation ZIP provides a matching mask; newer v2 image-only series remain available without labels.

FactValue
Cases487
Series487
Size6.3 GB
ModalityMR
Reference masksYes
LicenseCC-BY-4.0
PublisherThe Cancer Imaging Archive
Version2
DOI10.7937/HRZH-2N82

What you can do with it in MedSeg

Copied cases behave like normal project series - the public image bytes are linked, not duplicated, so copies are instant and take no extra storage.

  1. Copy cases into a project.
    Filter, multi-select, copy - reference masks come along if you want them.
  2. Run AI segmentation.
    TotalSegmentator, MRSegmentator, nnInteractive 3D clicks/scribbles, or text-prompted VoxTell.
  3. Edit and measure.
    Brush, lasso, fill, oblique planes, volumes in ml - in the browser.
  4. Train your own nnU-Net.
    Correct masks on a handful of cases and train a custom model on hosted GPUs.

Citation

If you use this dataset in your research, cite the source per its license terms.

Kujawa, A., Dorent, R., Wijethilake, N., Connor, S., Thomson, S., Ivory, M., Bradford, R., Kitchen, N., Bisdas, S., Ourselin, S., Vercauteren, T., & Shapey, J. (2023). Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Annotated Multi-Center Routine Clinical Dataset (Vestibular-Schwannoma-MC-RC) (Version 2) [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/HRZH-2N82

License: CC-BY-4.0

Open data still carries obligations - attribution at minimum. Check the license terms and the source publication before publishing work built on this dataset. MedSeg is a research tool, not a medical device.

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